Chapter 8: Changes in extremes

Chapter description

This chapter assesses past and future changes in Canada’s weather and climate extremes.

Chapter key messages

Plain language summary^{Footnote 1}

This chapter focuses on changes in weather and climate extremes across Canada, examining historical and future trends in these extremes, as well as their underlying causes. Extreme weather and climate events—such as the devastating 2021 heatwave in western Canada—are of great importance due to their profound impacts on human and natural systems. The chapter categorizes these extremes by duration (short-term weather events versus longer-term climate extremes) and complexity (single variable versus compound phenomena, which involve multiple factors acting together). It also includes some Indigenous perspectives on the consequences of changes in extremes, recognizing the importance of these perspectives in understanding and adapting to changes.

Confidence in assessments of past and projected trends varies with both the type of extreme event and the spatial scale considered. Confidence is generally higher for changes in variables like temperature extremes, which exhibit comparatively little spatial variability and are directly caused by the additional heat trapped in the atmosphere due to human-caused increases in greenhouse gas concentrations. Confidence is lower for changes in variables like surface wind speeds, which show strong spatial variability and are more affected by dynamical processes. Confidence also declines along a gradient of scale and is highest for national or regional assessments and lowest for local assessments, when observation networks are often sparse and internal climate variability can mask signals of long-term change. An additional constraint at finer scales is that many local extremes are controlled by physical processes (for example, atmospheric turbulence, individual thunderstorms, and complex topography) that are unresolved or only partially resolved in climate models designed for large-scale simulations, further limiting confidence in some variables.

Temperature extremes show clear trends across Canada: hot extremes are becoming more intense and frequent, while cold extremes are becoming less intense and frequent. These patterns can largely be attributed to human-caused climate change, particularly rising greenhouse gas concentrations. Projections indicate that these trends will continue, with cold extremes warming faster than hot ones, and with larger changes expected with greater increases in global temperature.

The water cycle describes the movement of water through the various components of the climate system. The assessment of changes in water cycle extremes, such as floods and droughts, is addressed in Chapter 5; key findings are included in this chapter for completeness. Extremes in precipitation—including one-day and five-day total precipitation and sub-daily rainfall events, as well as one-day snowfall—are assessed in this chapter. Observations and projections highlight increases in both the frequency and magnitude of total precipitation and rainfall extremes, driven by the atmosphere’s increased capacity to hold moisture due to climate warming. Regional variability exists, with northern Canada experiencing the largest relative increases. One-day heavy snowfall amounts have decreased in southwestern Canada consistent with climate warming, but have increased in northern Canada, where, despite climate warming, cold season temperatures remain below freezing; these changes are projected to continue.

The section on precipitation extremes also explores observed and projected changes in rare but consequential extreme freezing rain and severe hail events. Evidence of past changes in these variables in Canada is limited, due to sparse observational data. Extreme freezing rain is projected to increase across most of Canada, while future increases in severe hail are limited to some regions. In both cases, projections are uncertain due to high spatial variability and the complexity involved in modelling the physical processes that lead to these precipitation types.

The chapter also examines wind extremes, including those caused by different types of storms, including extratropical storms (low-pressure weather systems occurring outside the tropics, also commonly known as extratropical cyclones), post-tropical storms (cyclones that begin in the tropics but then move northward, for example, Hurricane Fiona), and small-scale phenomena such as thunderstorms. Observed trends in wind extremes are less consistent and less robust than those for temperature or precipitation, reflecting a more complex interplay of atmospheric drivers, large internal climate variability, and limited observational data. Certain regions of Canada could experience substantial changes in strong winds associated with extratropical and post-tropical storms, while changes in smaller-scale, thunderstorm-related winds remain uncertain.

Extremes related to Canada’s oceans and coasts include ocean wave heights and storm surges, marine heatwaves, deoxygenation, ocean acidification, and compound coastal flooding. Observed and projected changes in ocean extremes are assessed in detail in Chapter 7. Key messages are provided in this chapter for reference, and as background information for the assessment of compound coastal flooding—which is projected to increase due to rising sea levels and increasing heavy precipitation. The chapter also assesses compound wind and rainfall events, where the simultaneous occurrence of strong winds and heavy rainfall can lead to significant infrastructure damage. These events can be caused by the landfall of atmospheric rivers along Canada’s coasts, as discussed in Chapter 4. While past trends in the frequency and intensity of compound wind and rainfall events are difficult to assess due to limited data and studies, these events are projected to increase, due primarily to more frequent extreme rainfall, although there is limited confidence in the regional details.

This chapter highlights fire weather as an important type of compound extreme event, which is closely linked to temperature extremes, precipitation deficits, and wind patterns. In general, rising temperatures increase fire weather risks by causing the drying of vegetation and soil. In addition, periods of reduced precipitation before and during the fire season exacerbate the drying of potential fuels, which contributes to more severe and widespread wildfires. In particular, the chapter cites the 2023 wildfire season as an example of how compound events, such as dry conditions paired with high temperatures, can amplify fire intensity and spread. Wildfires pose significant risks to ecosystems, air quality, and communities. Projections indicate that these risks will grow with continued warming, through longer fire seasons and more extreme fire weather. Human-perceived heat stress (the combination of temperature and humidity) is another compound extreme that is amplified by warming and is also projected to increase in the future throughout Canada.

Overall, past and projected changes in most extremes in Canada are consistent with scientists’ expectations of physical changes in a warming climate and are similar to those reported in other high-latitude countries. These consistencies increase confidence in our understanding of changes in extremes in Canada and its regions.

8.1: Introduction

Assessing long-term changes in weather and climate extremes is crucial because these extreme events can pose significant hazards to both human and natural systems. The heatwave that occurred in the western United States and Canada in June 2021 (Lucarini et al., 2023; Malinina and Gillett, 2024; Philip et al., 2022; White et al., 2023) is a clear example of a weather extreme that had catastrophic impacts on humans and natural ecosystems and caused substantial economic damage (White et al., 2023) (see Box 8.2). In this chapter, “extremes” generally refer to events that are rare for a given location and time of year (Seneviratne et al., 2021). For instance, during the June 2021 heatwave, the village of Lytton, British Columbia, experienced a temperature of 49.6°C, breaking the record for the highest daily maximum temperature in Canada. This made the event an example of a hot temperature extreme. While record-breaking events are rare and typically categorized as extreme, not all extreme events are necessarily record breaking. In practice, events occurring multiple times a year may still be classified as extreme. For example, high percentiles, such as the 95th percentile of daily values of a climate variable, such as rainfall amount, are frequently used to identify extremes.

The extremes discussed in this chapter (Figure 8.1) can be categorized based on two criteria. First, we categorize them based on their duration and associated temporal scale: short-duration events are considered weather extremes and longer-duration events are considered climate extremes. Second, we categorize extremes based on whether they are defined by single variables or by more complex phenomena involving multiple variables (Figure 8.2).

Single-variable extremes assessed directly in this chapter include surface temperature (section 8.2); precipitation (rainfall, snowfall, freezing rain, and hail measured on various timescales; section 8.3); and wind speeds (section 8.4). Multi-variable extreme phenomena include cyclones moving northward from the tropics (North Atlantic hurricanes) and those forming outside the tropics (extratropical cyclones); severe convective storms (for example, thunderstorms); and several types of compound events (section 8.7). North Atlantic hurricanes and extratropical cyclones, as well as thunderstorms, are not assessed in detail in this chapter, but their influence on wind extremes is considered in the assessment of wind speeds (section 8.4); North Atlantic hurricanes, extratropical cyclones, and the thunderstorm environment are assessed primarily in Chapter 4. In terms of compound events, this chapter includes dedicated assessments of fire weather (section 8.7.1; Box 8.4), compound coastal flooding (section 8.7.2), compound wind and rainfall events (section 8.7.3; Box 8.5), and human-perceived heat stress (section 8.7.4), which refers to the combined effects of extreme temperature and humidity.

Other types of extremes—such as droughts and floods (except coastal floods), and ocean-related extremes—are assessed primarily in other chapters. In such cases, this chapter presents only key findings and provides cross-references to the relevant chapters. Specifically, droughts and floods are addressed in Chapter 5, while extremes related to the ocean are addressed in Chapter 7.

This chapter assesses changes in extremes across Canada, including past changes and the attribution of their causes, and projected changes (Figure 8.1). While the chapter deals with changes in many extremes, the main areas of focus for the assessments are the three core near-surface variables: temperature, precipitation, and wind. The chapter advances the state of knowledge relative to previous assessments, building particularly on Chapter 11 of the Working Group I contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) (IPCC AR6 WGI) (Seneviratne et al., 2021) and the first edition of Canada’s Changing Climate Report (CCCR2019) (Bush et al., 2019).

Generally, confidence in the assessment of such changes decreases as the focus becomes more specific. For instance, there is more confidence in trend estimates and projections of extremes at broader regional or national scales than in trend estimates and projections for specific locations (see the map in Figure 2.1, which shows the boundaries of the regions used in this chapter and in other chapters for regional climate analyses). This is because estimates at local scales are more affected by gaps in the understanding of relevant processes, inaccuracies in approximating effects that climate models cannot directly capture, and natural variability. These factors decrease the clarity and reliability of climate change signals in the data relative to the background noise, making it harder to identify past trends and future changes at the local scale. Moreover, scientists’ confidence is often higher in the direction of a change than in its magnitude.

The frequency and intensity of extremes are influenced by numerous factors, including solar radiation (a function of latitude), the specific characteristics of the terrain (such as elevation, land use, and the presence of water bodies), and internal climate dynamics resulting from the interplay of various physical processes that operate simultaneously (and possibly interact with each other) over different timescales (Sillmann et al., 2017). In recent decades, observed changes in extremes have been linked mainly to changes in greenhouse gas and aerosol concentrations from human activities, and land use and management (Bush et al., 2019; Seneviratne et al., 2021).

Some human-caused changes in the climate system are rather straightforward and direct (Figure 8.2). For instance, the well-documented increase in the frequency of heatwaves and daily hot extremes and the reduction in the intensity and frequency of cold extremes have been primarily attributed to regional and global warming trends. Other links, however, are more complex and indirect (Figure 8.2). For example, increases in the magnitude of extreme precipitation are influenced by increases in atmospheric water vapour, which result from the increases in evaporation and the moisture-holding capacity of the atmosphere that accompany atmospheric warming. However, precipitation extremes are also influenced by changes in the vertical stability of the atmosphere, variations in the frequency and intensity of different types of storms, and, more broadly, by changes in large-scale atmospheric circulation patterns, including remote circulation anomalies known as teleconnections (assessed in Chapter 4). Changes in atmospheric circulation also affect the frequency and intensity of storms, and of other phenomena such as droughts and floods (Bush et al., 2019; Bush and Lemmen, 2019; Seneviratne et al., 2021). In addition, changes in storms might lead to changes in ocean waves, which can be compounded by higher sea levels resulting from the thermal expansion of warming ocean waters and the addition of water to the oceans from mass losses from glaciers and ice sheets (Fox-Kemper et al., 2021). Figure 8.2 aims to illustrate, in a simplified way, how human activities can trigger intricate chains of processes that ultimately alter the occurrence of meteorological phenomena and variables, including weather and climate extremes.

This chapter provides important background information that can help readers understand the content of Chapter 10, which focuses on climate services and the use of climate information. This is particularly relevant because extremes often represent major hazards that have substantial impacts on both human and natural systems. These impacts, however, depend not only on the severity of a given hazard, but also on the level of human and natural exposure to the hazard and on the vulnerability of the exposed systems. For instance, Indigenous communities often face disproportionate impacts from climate change, including changes in extreme events, because they are often more exposed and vulnerable. This can result from both greater exposure—due to residence in high-latitude or remote regions, floodplains, or areas with permafrost-based infrastructure, and close connections to the land (see Case Story 6.1)—and greater vulnerability, due to limited access to services, under-resourced infrastructure, and systemic inequalities.

Figure take-away: A visual snapshot of the contents of this chapter and of important cross-chapter linkages.

Figure title: Visual guide to the content of Chapter 8 and key cross-chapter linkages

Figure 8.1: Visual guide to Chapter 8 and cross-chapter linkages

Figure take-away: Weather and climate extremes encompass a wide range of specific phenomena and variables, each influenced by multiple large-scale changes driven by human activities.

Figure title: Human activities and weather and climate extremes

Figure 8.2: Simplified flow diagram showing relationships between human activities, large-scale drivers, and weather and climate extremes in Canada.

The information used in this chapter to assess observed changes in extremes is mainly based on three lines of evidence: observation-based datasets (including station and reanalysis data); the understanding and theory of physical processes; and climate models, which are often used for studies on the attribution of climate change to human and natural causes. Section 2.3 describes in detail the observation-based data products that can be used to analyze past changes. However, analyzing changes in extremes from an observational perspective poses particular challenges, due to the rarity of these events and the wide range of time frames and spatial scales at which they occur. Observed changes in extremes are generally based on station and reanalysis data, owing to the limited length of observational datasets from other sources, such as satellites and radar. Extreme events involving atmospheric (for example, temperatures, precipitation, and wind speeds), ocean, or cryosphere variables are typically defined based on daily data, although sub-daily data are also used for precipitation and wind speeds.

In this chapter, estimated past and future changes in extremes will be presented based on fixed time periods (for example, past changes in a given extreme over the last 50 years) and fixed global warming levels (for example, future changes in a given extreme based on a global average temperature that is 2°C greater than in a pre-industrial climate). The specific way that global warming levels are calculated, and their usefulness, is described in detail in Box 3.1.

8.2: Temperature extremes

Key Message 8.1: The intensity and frequency of hot extremes in Canada as a whole and in multiple regions have increased since the mid-20th century (high confidence).^{Footnote 2} The intensity and frequency of cold extremes in Canada as a whole and in all Canadian regions have decreased since the mid-20th century (high confidence). Human influence on the climate is the dominant driver of the observed warming of both hot and cold extremes (high confidence).

Key Message 8.2: Increases in the intensity and frequency of hot extremes and decreases in the intensity and frequency of cold extremes are projected for all regions of Canada, with changes becoming larger as global mean temperature increases (very high confidence).

This section assesses changes in temperature extremes across Canada. Following much of the literature and previous assessments (Seneviratne et al., 2021; X. Zhang et al., 2019), we use standard annual indices to describe temperature extremes based on daily data. Hot extremes are represented by the highest daily maximum temperature of the year, which is the maximum temperature on the hottest day of the year. Cold extremes are represented by the lowest daily minimum temperature of the year, which is the minimum temperature on the coldest night of the year. While these indices provide a general picture of the changes in hot and cold extremes, other options are available and may be more relevant for particular impacts. For example, we also assess the number of hot days each year, determined by counting the number of days on which the maximum temperature exceeds 30°C, and the number of cold nights each year, by counting the number of nights with a minimum temperature below -30°C. This section also considers heatwaves and cold spells (that is, multi-day hot and cold extremes).

Extreme temperature events are usually associated with large-scale meteorological patterns that affect local weather conditions (Grotjahn et al., 2016). In addition, local and regional forcings and feedback mechanisms can moderate or amplify extreme temperatures (Barriopedro et al., 2023; Donat et al., 2017; R. E. Stewart et al., 2017). For instance, drier soils associated with drought conditions lead to higher sensible heat fluxes (the conductive heat flux from the Earth’s surface to the atmosphere) and hotter temperatures. In addition, direct human changes, such as deforestation (Lejeune et al., 2018) and irrigation (Thiery et al., 2017), can also affect extreme temperatures. In Canada, the large-scale meteorological patterns associated with extreme temperature events typically involve a highly undulating jet stream (Fragkoulidis et al., 2018; Grotjahn et al., 2016; B. Yu et al., 2023). Some undulations bring cold air from the north, and result in cold air outbreaks (large-scale extreme cold periods). In summer, other undulations result in heatwaves, specifically when a persistent high-pressure system (an atmospheric block) brings warm, moist air from the south as well as causing warming from subsiding air (air moving from higher up in the atmosphere toward the ground) and radiative heating (Pfahl and Wernli, 2012). The frequency, intensity, and persistence of these meteorological conditions are strongly affected by large-scale modes of internal climate variability, such as the Pacific Decadal Oscillation (Kamae et al., 2014). More information on changes in atmospheric circulation and the role of internal climate variability can be found in Chapter 4.

8.2.1: Past changes and attribution

CCCR2019 assessed that “extreme warm temperatures have become hotter, while extreme cold temperatures have become less cold” and that this is consistent with a warming climate (X. Zhang et al., 2019). Additional evidence since that report further supports the increase in hot extremes and decrease in cold extremes. For hot extremes, the highest daily maximum temperature, averaged for the country, increased at a rate of 0.15°C/decade (95% uncertainty range: 0.06 to 0.25°C/decade) between 1949 and 2023 (Table 8.2). The largest increases were observed in northern Canada, as well as in western and Atlantic parts of the country (Figure 8.3). Slight decreases in the highest daily maximum temperature were observed in the southern Prairies, although the average for the entire Prairie region (Alberta, Saskatchewan, and Manitoba) shows a slight increase. For cold extremes, the lowest daily minimum temperature, averaged for the country as a whole, increased at a rate of 0.50°C/decade (95% uncertainty range: 0.34 to 0.68°C/decade) between 1949 and 2023 (Table 8.2). The lowest daily minimum temperature warmed in all regions of the country, with the largest changes in northern and western Canada (Figure 8.3).

Cold extremes are warming faster than hot extremes, which is consistent with the patterns seen in the global average and in nearly all global land regions as assessed in Chapter 11 of the IPCC AR6 WGI report (Seneviratne et al., 2021). The coldest winter days have also warmed faster than winter average temperatures (assessed in Chapter 2, section 2.4), which is linked to decreases in day-to-day temperature variability (Blackport and Fyfe, 2024). The faster warming of cold extremes (compared to hot extremes and average temperatures) and decreased temperature variability are caused by the reduction in the north-south temperature gradient, part of the phenomenon of Arctic amplification (Blackport and Fyfe, 2024; Gross et al., 2020; Screen, 2014). Specifically, the coldest days warm faster because the cold, Arctic air displaced southward during cold air outbreaks is warming faster than air in the mid- and lower latitudes (Chapter 4, section 4.2).

The changes observed in temperature extremes in Canada are consistent with the increase observed in the intensity and frequency of hot extremes and the decrease in the intensity and frequency of cold extremes reported globally and in most global land regions in Chapter 11 of the IPCC AR6 WGI report (Seneviratne et al., 2021). The IPCC AR6 regions are not based on geopolitical boundaries. For reference, Figure 3 of Canada’s Changing Climate Report in Light of the Latest Global Science Assessment [PDF, 1.1 MB] (Bush et al., 2022) shows the IPCC AR6 reference regions overlaid on a map of Canada. Areas of Canada north of 50°N latitude are included in the Northeast North America and Northwest North America regions. Changes in extreme values in the land regions referred to in the IPCC AR6 as Eastern North America and Central North America include changes in parts of southern Canada, but are largely dominated by the changes occurring in the United States. Although changes in hot extremes were variable across North America, many parts of the continent, including the two IPCC AR6 regions covering most of Canada, have seen increases; in addition, cold extremes have warmed across the continent (Seneviratne et al., 2021).

Figure take-away: Both hot and cold extremes have warmed across Canada, with cold extremes undergoing greater changes, while hot extremes increased in frequency and cold extremes decreased in frequency.

Figure title: Observed changes in temperature extremes

Figure 8.3: Maps showing trends in extreme temperature indices at stations across Canada, expressed as the rate in units (°C or days, depending on the index) per decade, during the 1949 to 2023 period. Indices shown are the a) highest daily maximum temperature each year; b) lowest daily minimum temperature each year; c) number of hot days (maximum temperature > 30°C) each year; and d) number of cold nights (minimum temperature < -30°C) each year. The data are from the third generation homogenized temperature dataset for Canada, updated through 2023 as in Chapter 2. The calculation of indices and trends closely follows Vincent et al. (2018) and CCCR2019, with an update to the tolerance of missing annual data allowed in the calculation of trends. The colour of the points at each station location indicates the magnitude of the trend. Filled circles indicate significant trends at the 95% confidence level (that is, ≤ 5% chance of concluding that an effect or trend exists when it does not). The grey dashes in c) and d) represent stations that did not experience enough hot days or cold nights for trends to be calculated. A version of this figure using the fourth generation homogenized temperature dataset is very similar and can be found in the Supplementary Material. Data source: updated from (Vincent et al., 2020).

Hot extremes are typically considered heatwaves when several hot days occur consecutively. There is no single definition of a heatwave, as heatwave days can be defined by temperatures that exceed locally defined relative thresholds (for example, climatological percentiles that vary by region and season) or absolute thresholds (for example, associated with heat warning criteria or health impacts) over a certain number of days. For Canada as a whole, heatwaves defined as at least three days exceeding climatological thresholds have increased in frequency and duration, as well as in cumulative heat exposure (a measure of accumulated heatwave intensity) (Kirchmeier-Young et al., 2025). Significant trends were also detected for many regions, but can depend heavily on the type of heatwave event considered (Kirchmeier-Young et al., 2025). Heatwave intensities across North America are increasing at a rate similar to the percentile thresholds used to define heatwaves, but in addition to shifts in the average temperatures, changes in the variability of maximum temperatures are required to explain regional patterns (Comeau et al., 2026). The most extreme heatwaves have more consistent regional patterns (Comeau et al., 2026). Lee et al. (2021) also demonstrated that the frequency of extreme heat events was increasing, with the strongest changes in eastern Canada. Increasing heatwaves in Canada are consistent with an accelerating increase in many heatwave metrics during the 1950 to 2017 period, both globally and in most IPCC AR6 regions (Perkins-Kirkpatrick and Lewis, 2020). High humidity accompanying hot extremes can increase the impacts of a heatwave; this type of compound event is discussed in greater detail in section 8.7.4.

Cold waves, or cold spells, can be defined as a fixed number of consecutive days of cold extremes. Similar to heatwaves, the threshold and duration used to define cold waves can be identified in different ways. When defined as six or more days with minimum temperatures below the climatological 10th percentile, the duration of cold spells in Canada as a whole decreased at a rate of 0.66 days/decade (95% uncertainty range: -0.94 to -0.38 days/decade) between 1949 and 2023. Cold air outbreaks in Canada have decreased in intensity, frequency, duration, and spatial extent (Smith and Sheridan, 2020). This is consistent with the strong warming in cold waves observed throughout the northern mid-latitudes, representing three to five times the rate of global average temperature increase during the 1900 to 2018 period (Van Oldenborgh et al., 2019).

CCCR2019 determined that “most of the observed increase in the coldest (likely) and warmest (high confidence) daily temperatures of the year in Canada from 1948 to 2012 can be attributed to anthropogenic influence” (X. Zhang et al., 2019). This is broadly supported by more recent literature, which uses updated models and the global land regions from the IPCC AR6 WGI report, although attribution could depend on the variable and region selected (Seong et al., 2021, 2022). The observed changes in both hot and cold extremes throughout Canada are only reproduced when climate model simulations include human influence. The 1.2°C increase in the highest daily maximum temperature during the 1949 to 2023 period can be attributed to anthropogenic forcing, as can the 2.9°C increase in the lowest daily minimum temperature (Figure 8.4). In addition to increases in the average temperature (Chapter 2, section 2.4.2), decreases in day-to-day temperature variability, predominantly during the cool season, have also been attributed to anthropogenic forcing, specifically to human emissions of greenhouse gases (Blackport et al., 2021; Wan et al., 2021).

Anthropogenic (human-caused) warming, including Arctic amplification, may affect large-scale modes of internal climate variability that influence the occurrence of temperature extremes; however, there is low confidence in the net impact of anthropogenic warming on the large-scale waves associated with temperature extremes. Chapter 4 discusses observed and projected changes in the large-scale drivers associated with temperature extremes in greater detail. Although changes in these drivers are uncertain, the direct effect of a warming climate, which results in warmer extremes, is the main factor determining changes in temperature extremes across Canada.

Regarding the attribution of individual extreme events, the IPCC AR6 WGI report stated that “some recent hot extreme events would have been extremely unlikely to occur without human influence on the climate system” (Seneviratne et al., 2021). This includes events like the 2021 heatwave in western Canada (see Box 8.2).

Figure take-away: Changes in the highest and lowest daily temperatures in Canada can be attributed to human-caused climate change.

Figure title: Attributed changes in temperature extremes in Canada

Figure 8.4: Bar plots comparing observed changes in temperature extremes (in °C) in Canada with changes simulated by models driven by anthropogenic or natural forcings. The changes for 1949 to 2023 are shown for a) the highest daily maximum temperature of the year and b) the lowest daily minimum temperature of the year. The values obtained from CMIP6 model simulations using all forcings are shown in gold and the values from simulations using natural forcings only, in green; the observed changes for the same period are shown in grey. The top of the bar represents the attributed change, and the vertical black line shows the 90% uncertainty range. The methods are similar to those used for Figure 4.5 of CCCR2019. Data sources: Eyring et al. (2016); Gillett et al. (2016); Wan et al. (2025).

8.2.2: Future changes

The increasing intensity and frequency of extreme hot temperatures and the decreasing intensity and frequency of extreme cold temperatures are projected to continue across Canada. These changes are expected to be greater at higher levels of global temperature increase (Figure 8.5). Even a half degree of increase in the global temperature (for example, the difference between 2°C and 1.5°C of warming) is projected to result in notable changes in extreme temperatures in Canada. Note that projected changes are presented here as a summary over a number of years, in order to represent the overall change in a warming climate; individual extreme events are also affected by weather variability and we may therefore see large anomalies on top of this background warming, especially at local scales.

Figure take-away: In the future, the highest and the lowest daily temperatures in Canada will be warmer, with greater changes occurring in cold extremes.

Figure title: Projected changes in temperature extremes in Canada

Figure 8.5: Maps showing a) values in the recent past, and b) and c) projected changes, for the highest daily maximum temperature of the year (hottest day) and the lowest daily minimum temperature of the year (coldest night) (in °C) across Canada. The three maps in b) and c) show the projected increase (representing the ensemble median) relative to the a) recent past (that is, 1°C of global warming from the pre-industrial level) at three different levels of global warming (1.5°C, 2°C, and 4°C from the pre-industrial level). The different colours shown on the maps and explained in the legends represent a) the daily maximum and minimums, and b) and c) changes relative to these levels. See Chapter 3, Box 3.1, for an explanation of how global warming levels translate to increases in Canadian average temperatures over different time periods. Projections are based on CanDCS-M6, an ensemble of 26 downscaled (1/12°) and bias-corrected CMIP6 models. Data source: Sobie et al. (2024).

The highest daily maximum temperature of the year (hottest day of the year) is projected to increase in all Canadian regions (Figure 8.6). Changes are projected to be larger with greater increases in the global average temperature. In the recent past, the average highest daily maximum temperature for the country as a whole was 26.3°C (80% uncertainty range: 26.08−26.76°C) (that is, at 1°C of global temperature increase from the pre-industrial level). This highest daily maximum is projected to warm by an additional 0.89°C (80% uncertainty range: 0.43−1.28°C) at 1.5°C of global warming, by an additional 1.6°C (80% uncertainty range: 1.18 to 2.41°C) at 2°C of warming, and by an additional 5.06°C (80% uncertainty range: 3.80 to 6.58°C) at 4°C of warming. The rate of warming is projected to be similar to that in the Canadian average summer temperature (Chapter 3, section 3.4.2), both at a faster rate than the global average temperature. These projections apply to Canada as a whole; however, some regions of the Canadian Arctic have been identified as regions where the most extreme hot extremes are warming faster than more moderate extremes, though this is not well represented in climate models (Kornhuber et al., 2024). Despite the different values for the highest daily maximum temperature, the relative changes from the recent past are similar across regions. With much larger changes projected under 4°C of global warming, limiting global temperature increases will prevent large increases in hot extremes across Canada.

The lowest daily minimum temperature of the year (coldest night of the year) is projected to increase in all Canadian regions with every increment of global warming (Figure 8.6), with these changes greater at higher levels of global temperature increase. The average lowest daily minimum temperature of the year for the country as a whole was -38.54°C (80% uncertainty range: -38.93 to -37.78°C) in the recent past (that is, at 1°C of global temperature increase from the pre-industrial level). This daily minimum is projected to warm by an additional 1.39°C (80% uncertainty range: 0.54−2.23°C) at 1.5°C of global warming, by an additional 2.79°C (80% uncertainty range: 1.87−3.98°C) at 2°C of warming, and by an additional 9.33°C (80% uncertainty range: 6.78−11.49°C) at 4°C of warming. Cold extremes are projected to continue warming at a faster rate than hot extremes (Figure 8.6), a result that is consistent with the changes projected globally and in most regions of the world (C. Li et al., 2021a; Seneviratne et al., 2021), including those in other localized studies for Canada and neighbouring regions (Herman-Mercer et al., 2020; Wazneh et al., 2020). Similarly, the coldest nights in fall, winter, and spring are projected to warm faster than the corresponding seasonal averages in Canada as a whole (Gross et al., 2020), consistent with the anticipated reduction in day-to-day temperature variability during the cool season.

Figure take-away: Both the highest daily maximum and lowest daily minimum temperatures will warm in all regions of Canada with every increment of global warming, but cold extremes will see greater increases.

Figure title: Projected changes in the intensity of temperature extremes

Figure 8.6: Bar charts showing changes in the hot and cold extremes for Canada as a whole and by region (regional averages). a) Highest daily maximum temperature of the year (hottest day of the year) and b) lowest daily minimum temperature of the year (coldest night of the year), at different global average temperature increases relative to the recent past (that is, 1°C global warming). See Chapter 3, Box 3.1, for an explanation of how global warming levels translate to increases in Canadian average temperatures over different time periods. Projections are based on CanDCS-M6, an ensemble of 26 downscaled (1/12°) and bias-corrected CMIP6 models. The top of each bar shows the ensemble median, with the 80% uncertainty range^{Footnote 3} of the ensemble results illustrated by the vertical black line. Regional values and ranges can be found in the Supplementary Tables. Data source: Sobie et al. (2024)

Both the highest daily maximum and lowest daily minimum temperatures are projected to warm in proportion to the increase in global average temperature (Figure 8.6). This linear scaling between changes in regional temperature extremes and in the global average temperature is found in all global land regions and is independent of the emissions scenario used in climate model projections (Seneviratne et al., 2021). The most robust observations and downscaled and bias-corrected model simulations for analyzing extremes across Canada are available from the mid-20th century onwards. However, because there is a linear relationship between changes in temperature extremes and changes in global average temperature, we can estimate the warming in Canadian extremes since the pre-industrial period. This allows us to make projections over the same period used for projected changes in average temperatures (Chapter 3, section 3.4) and many other variables summarized in Chapter 3.

We used a collection of downscaled and bias-corrected model simulations to calculate the slope of the linear relationship between the change in temperature extremes across Canada relative to 1°C of warming in the recent past and the change in global average temperatures from pre-industrial levels. This method is similar to that described in Seneviratne et al. (2021). We estimated a 1.7°C increase in the highest daily maximum temperature for Canada as a whole for every 1°C increase in global average temperature. This rate of change was then used to estimate the changes in average hot extremes in Canada relative to pre-industrial levels that are expected to accompany the increase in global average temperatures under the scenarios and time periods assessed in Chapter 3. In the near term (2021 to 2040), the highest daily maximum temperature in Canada as a whole is projected to increase by 2.6°C (90% uncertainty range: 2.0 to 3.1°C) relative to the pre-industrial level. By the late-century period (2081 to 2100), the expected increase is 3.1°C (90% uncertainty range: 2.2−4.1°C) under the low emissions scenario (shared socio-economic pathway 1-2.6, or SSP1-2.6), 4.6°C (90% uncertainty range: 3.9−6.0°C) under the intermediate emissions scenario (SSP2-4.5), and 6.2°C (90% uncertainty range: 4.8−7.9°C) under the high emissions scenario (SSP3-7.0). These changes in hot extremes are similar to the changes in average temperature in Canada in each scenario and greater than the increases in global average temperature (Chapter 3, section 3.4, Box 3.1). According to the same method, the estimated lowest daily minimum temperature in Canada as a whole is expected to increase by 3.1°C for every 1°C increase in global average temperature above the pre-industrial level. In the near term, the lowest daily minimum temperature in Canada as a whole is projected to increase by 4.7°C (90% uncertainty range: 3.7−5.6°C) relative to the pre-industrial level. By the late-century period, the projected increases are 5.6°C (90% uncertainty range: 4.0−7.4°C) under the low emissions scenario (SSP1-2.6), 8.4°C (90% uncertainty range: 6.5−10.9°C) under the intermediate emissions scenario (SSP2-4.5), and 11.2°C (90% uncertainty range: 8.7−14.3°C) under the high emissions scenario (SSP3-7.0). The warming of cold extremes above pre-industrial levels is projected to be greater than the changes in both the hot extremes and in the Canadian average temperature.

In addition to changes in the intensity of hot and cold extremes, the frequency of such events is also changing. In a warming climate, hot extremes are occurring more often (an increase in frequency) and cold extremes are occurring less often (a decrease in frequency). An increase in the number of hot days and a decrease in the number of cold nights are projected in all Canadian regions as global temperatures rise (Figure 8.7). Hot days and cold nights are defined using absolute temperature thresholds that are the same for all regions (30°C for hot and -30°C for cold).

Figure take-away: An increase in the number of hot days and a decrease in the number of cold nights are projected in all regions of Canada with every increment of global warming.

Figure title: Projected number of hot days and cold nights in Canada

Figure 8.7: Bar plots showing values for Canada as a whole and average regional values for the number of a) hot days and b) cold nights projected to accompany different global average temperature increases above the pre-industrial level. Hot days are defined as days when the maximum temperature exceeds 30°C and cold nights, as nights when the minimum temperature is below -30°C. Note that values are expressed as the number of days rather than as a change. See Chapter 3, Box 3.1, for an explanation of how global warming levels translate to increases in Canadian average temperatures over different time periods. Projections are based on CanDCS-M6, an ensemble of 26 downscaled (1/12°) and bias-corrected CMIP6 models. The median value for the ensemble is shown, with the vertical black bar representing the 80% uncertainty range for the entire ensemble. Regional values and ranges can be found in the Supplementary Tables. Data source: Sobie et al. (2024).

Since different regions in Canada experience vastly different numbers of days of hot and cold extremes as defined by absolute temperatures, another way to consider the changing frequency of temperature extremes is to consider the highest or lowest temperature values (that is, the highest daily maximum or lowest daily minimum) that occurred at a given frequency in the recent past. Hot extremes that occurred on average once in 10, 20, and 50 years in the recent past (that is, at a 1°C global average temperature increase relative to the pre-industrial level) were defined using a large ensemble of bias-corrected climate model simulations. At higher levels of global warming, the frequency of such events is projected to increase (Figure 8.8a). For example, at 2°C of global warming, the event that occurred on average once in 20 years in the recent past in Canada as a whole is projected to occur about 3 times in 20 years. By 4°C of global warming, that same event is projected to occur more than 10 times in 20 years (or on average every two years). Similar increases in frequency are projected in all Canadian regions for every increment of global warming (Figure 8.8b). At higher levels of global warming, events that were rare in the recent past are projected to become a common occurrence nationally and in all regions. Conversely, cold extremes that were similarly rare in the recent past are projected to become very rare to virtually nonexistent at higher levels of global warming. A greater change in frequency is projected for rarer events (that is, greater for an event that occurred once in 50 years in the recent past compared to an event that occurred once in 10 years). This projected pattern of greater changes in the frequency of rarer hot and cold extremes is also seen globally and in most land regions around the world (C. Li et al., 2021a; Seneviratne et al., 2021).

Figure take-away: Hot extremes are projected to occur more frequently in all Canadian regions with every increment of global warming, with greater changes for rarer events.

Figure title: Projected changes in the frequency of hot extremes in Canada

Figure 8.8: a) Chart showing projected changes in the frequency of hot extremes in Canada as a whole by increment of global warming and b) bar plots showing projected changes by region. Frequency is defined as the number of times in which the highest daily maximum temperature that occurred, on average, once in 10, 20, and 50 years in the recent past (that is, 1.0°C of global warming since the pre-industrial era, approximated in this report as the period from 1850 to 1900) is projected to occur under different global warming levels (1.5°C to 4°C). a) Results for Canada as a whole, expressed as the number of times an event of the given magnitude is projected to occur at each global warming level (adapted from Figure SPM.6 of the IPCC AR6 WGI report). The top row shows the frequency in 10 years (with 10 dots), the middle row in 20 years (with 20 dots) and the bottom row in 50 years (with 50 dots). Dots in each grouping are shaded darker to indicate the average number of occurrences, with text indicating the value just below. b) Results for Canada as a whole and by region, with the top of each bar indicating the number of times the given event is projected to occur, on average, during the designated time period at each global warming level. The different colours of the bars indicate events of different rarity in the recent past climate (the 10-, 20-, and 50-year events). Values were obtained from CanDCS-M6, an ensemble of 26 downscaled (1/12°) and bias-corrected CMIP6 models, and were computed using the methods in Kharin et al. (2018). Data source: Sobie et al. (2024).

8.2.3: Confidence terms in key messages: summary of evidence

Key Message 8.1: The intensity and frequency of hot extremes in Canada as a whole and in multiple regions have increased since the mid-20th century (high confidence). The intensity and frequency of cold extremes in Canada as a whole and in all Canadian regions have decreased since the mid-20th century (high confidence). Human influence on the climate is the dominant driver of the observed warming of both hot and cold extremes (high confidence).

Key Message 8.2: Increases in the intensity and frequency of hot extremes and decreases in the intensity and frequency of cold extremes are projected for all regions of Canada, with changes becoming larger as global mean temperature increases (very high confidence).

Our assessment of changes in hot and cold extremes in Canada as a whole is supported by multiple lines of evidence, including model simulations, analyses of observations, and the understanding of physical processes. Furthermore, our assessment is consistent with that in the IPCC AR6 WGI report and in CCCR2019. Observed changes in annual hot and cold extremes were calculated for this report based on homogenized datasets of station-based observations across Canada. Extensive effort has been put into developing such datasets, ensuring their quality, and removing the influence of any non-climatic factors (Chapter 2, section 2.3, Box 2.1). The analysis methods closely follow those described in Vincent et al. (2018), with an update to the tolerance for missing annual data that is used to calculate trends that only impacts the station-level trends. The trends in cold extremes are strong and consistent across the country in both observed temperature datasets used, leading to an assessment of high confidence in both the national and regional changes. The trends in hot extremes are statistically significant in both datasets when averaged nationally and the dominant pattern across station-level trends is for increases in hot extremes across much of the country. This supports high confidence in the national-level changes. While there are some stations and areas with decreases in hot extremes, averages over all regions considered show positive trends, with many significant. The two datasets broadly agree on increases in hot extremes across Canada. Our assessment for the statement “hot extremes have increased in multiple regions” considers regional changes in aggregate, which allows for a high confidence statement. We note this confidence does not necessarily apply when considering the changes in any particular region alone. The observed changes in Canada derived from station data are consistent with the changes assessed in the IPCC AR6 WGI report for other global and continental regions. Climate model simulations generally reproduce the observed warming of both hot and cold extremes only when human forcings (including greenhouse gases, land use, and aerosols) are included in the simulations. This further supports our confidence in the past changes identified; combined with the consistent results in other studies and regions, and at other spatial scales, we have high confidence in the attribution of these changes to human influence on the climate.

The characterization of future changes in hot and cold extremes is assessed as having very high confidence, due to the consistency across models, studies, other assessments, expectations based on physical processes, projected changes across similar regions, and past changes. Projected changes in hot and cold extremes are presented based on downscaled and bias-corrected climate model simulations. The bias-corrected model simulations agree more closely with the observations than the raw model output. Model ensemble members consistently project increases in the frequency and intensity of hot extremes and decreases in the frequency and intensity of cold extremes, especially at higher levels of global warming. The projections presented in this section—including the linear relationship between global average temperature and the intensity of temperature extremes, as well as the rarity-dependent relationship between global average temperature and the frequency of temperature extremes—are consistent with those from the IPCC AR6 WGI report and other published studies. The projected changes for Canada are also consistent with those for other mid- and high-latitude land regions around the globe. Furthermore, the changes in the intensity and frequency of temperature extremes are in line with physical expectations based on a warming climate and are a continuation of the observed trends.

8.3: Precipitation extremes

Precipitation extremes and past and future changes in these extremes are affected by processes that operate on spatial and temporal scales ranging from micrometres (one millionth of a metre) and seconds (for example, cloud microphysics) to thousands of kilometres and days (for example, mid-latitude cyclones and fronts) (Chapter 4, Figure 4.1; Figure 8.2). These processes include changes in the moisture-holding capacity of the warming atmosphere, moisture availability, large-scale and local atmospheric circulation, and the thermal structure of the atmosphere, which dictates whether precipitation falls in liquid, freezing/mixed phase, or frozen form (R. E. Stewart et al., 2015). Because of this complexity, precipitation extremes are assessed here in separate sub-sections, by individual variables, including one-day and five-day extreme total precipitation (section 8.3.1), sub-daily extreme rainfall (section 8.3.2), one-day snowfall extremes (section 8.3.3), freezing rain extremes (section 8.3.4), and hail (section 8.3.5). Other types of precipitation, such as snow pellets and mixed rain and snow, or related hazards, such as adhering snow (Hanesiak et al., 2022; R. Stewart et al., 2023), are not addressed. Note that changes in many of the meteorological drivers of precipitation extremes in Canada, such as tropical and extratropical cyclones, atmospheric rivers, and the thunderstorm environment that leads to severe convective storms, are assessed in Chapter 4.

8.3.1: One-day and five-day total precipitation extremes

Key Message 8.3: The intensity and frequency of one-day and five-day precipitation extremes have increased in Canada as a whole since the mid-20th century (medium confidence), consistent with an increase in atmospheric moisture across Canada due to warming. Human influence on the climate is the main driver of the observed intensification of extreme precipitation at the continental scale across North America (high confidence). Increases in the intensity and frequency of one-day and five-day precipitation extremes have been observed with low confidence in many, but not all, regions of Canada since the mid-20th century. These regional changes are uncertain, due to large spatial and temporal variability.

Key Message 8.4: Increases in the frequency and intensity of one-day and five-day precipitation extremes are projected for all regions of Canada (high confidence), with changes becoming larger as global average temperature increases (high confidence).

This section assesses changes in annual maximum one-day and annual maximum five-day total precipitation, as well as in the frequency of heavy precipitation days (defined here as the annual number of days when precipitation ≥ 10 mm). As noted in CCCR2019, the climate science literature contains a substantial body of literature concerning historical and future changes in these measures of extreme precipitation. All three are standardized indices that are calculated and reported globally and are regularly assessed in IPCC reports, including Chapter 11 of the IPCC AR6 WGI report (Seneviratne et al., 2021). This body of research provides a solid foundation for assessing precipitation extremes at the one-day and five-day timescales. While intermediate-duration events are also relevant, the lack of standardized indices limits our ability to assess changes in these events.

In general, precipitation extremes at the one-day timescale can cause localized flood damage to infrastructure, such as roads and buildings, while longer episodes of heavy precipitation can produce flooding across larger regions—for example, the flooding that occurred during the consecutive atmospheric rivers that affected southern British Columbia in November 2021 (Chapter 4, section 4.5). Depending on where precipitation falls, one-day and longer extremes can also be beneficial for replenishing critical water supplies for ecosystems and people. The effects of changes in extreme precipitation on the water cycle of Canada are assessed in Chapter 5.

8.3.1.1: Past changes and attribution

CCCR2019 (X. Zhang et al., 2019) concluded that there “do not appear to be detectable trends in short-duration [one-day] extreme precipitation in Canada for the country as a whole based on available station data. More stations have experienced an increase than a decrease … but the direction of trends is rather random over space.” Five-day precipitation extremes were not assessed. These results were based on assessments of unhomogenized station data from records extending to the end of 2005 (Shephard et al., 2014) and 2012 (Vincent et al., 2018).

Since the publication of CCCR2019, a homogenized daily precipitation dataset (1949 to 2023) has been compiled for Canada (Chapter 2, section 2.3.4.2) (X. L. Wang and Feng, 2026; X. L. Wang et al., 2023, 2026) and trends have been calculated for a series of extreme precipitation indices, including annual maximum one-day and five-day precipitation and the annual number of heavy precipitation days. Both annual maximum one-day precipitation (Figure 8.9a) and five-day precipitation (Figure 8.9b) amounts have increased significantly at most stations in Canada, although stations in the Rocky Mountains and southern Prairies show insignificant decreases from 1949 to 2023. Large and statistically significant increases were found in the Maritime provinces and central to northern Canada. The annual number of heavy precipitation days has increased significantly at most stations in Canada (Figure 8.9c). The greater spatial consistency of trends is attributed to the correction of inhomogeneities in the series of station data used to construct the dataset, as well as the extension of the record by 11 or more years, which has increased statistical power.

Figure take-away: One-day and five-day precipitation extremes have increased significantly at most stations in Canada.

Figure title: Observed changes in precipitation extremes across Canada

Figure 8.9: Maps showing trends observed in annual maximum a) one-day and b) five-day precipitation and in the c) number of days with precipitation over 10 mm during the 1949 to 2023 period. Trends were calculated as the change in mm/day in a) annual one-day or b) five-day maximum precipitation, or the change in the c) number of days with precipitation over 10 mm, over the 1949 to 2023 period, and were then expressed in % change per decade relative to the average maximum one-day or five-day precipitation observed, or the number of days with precipitation over 10 mm, during the 1971 to 2000 baseline period. The colour of the point at each station location indicates the magnitude of the trend. Trends that are significant at the 5% level (that is, ≤ 5% chance of concluding that an effect or trend exists when it does not) are shown with filled circles. Data source: X.L. Wang and Feng (2026).

Overall, the results for Canada are consistent with the body of literature on one-day and five-day precipitation extremes. This includes updated analyses of long records of global precipitation data (Sun et al., 2021), which show that that the frequency and intensity of one-day and five-day precipitation events have increased at the global scale in most land regions with good observational coverage, and at the continental scale in North America. Trend scaling of one-day and five-day precipitation extremes aggregated for North America as a whole is broadly consistent with the expected increase in the moisture-holding capacity of the atmosphere described by the Clausius–Clapeyron relation (Sun et al., 2021) (Box 8.3). Observed increases in the frequency and magnitude of atmospheric rivers in Canada are also consistent with warming-driven changes in atmospheric moisture (Chapter 4, section 4.5).

The observational evidence for detectable changes in large-scale atmospheric circulation conditions that might affect extreme precipitation—aside from the changes in atmospheric rivers—is scant. The one possible exception is the slight northward shift in the position of mid-latitude storm tracks in the Northern Hemisphere (Chapter 4, section 4.3.2). Climate modelling studies have examined the separate contributions of thermodynamic and dynamical processes to projected changes in extreme precipitation this century under the very high emissions scenario (SSP5-8.5) (that is, under climate conditions with a much stronger anthropogenic signal than in the historical period). Thermodynamic processes are the dominant influence on these changes in mid- and high-latitude areas in the Northern Hemisphere, including all of Canada, with dynamical processes possibly slightly counteracting the effects of thermodynamic processes (Norris et al., 2019; Pfahl et al., 2017; Tandon et al., 2018).

According to the bulk of the evidence, precipitation extremes have intensified in North America in a way that is consistent with the thermodynamic influence of observed warming on atmospheric moisture (Steinschneider and Najibi, 2022; Sun et al., 2021). This observational evidence is also supported by the results of formal detection and attribution analyses (Box 8.1). The observed intensification of one-day and five-day precipitation extremes is attributable to human influence on the climate in North America at the continental scale (S. Dong et al., 2021; Kirchmeier-Young and Zhang, 2020; Paik et al., 2020; Sun et al., 2022). This is consistent with similar attribution analyses at larger scales (Seneviratne et al., 2021). Some studies have detected human influence on the intensification of heavy precipitation at the regional scale, for example, in central and eastern North America, but the uncertainty is much greater at these smaller scales (Kirchmeier-Young and Zhang, 2020; Sun et al., 2022).

In the context of individual extreme events, heavy precipitation is usually analyzed in association with flooding (Chapter 5, section 5.7). Since the publication of CCCR2019, event attribution studies have been published on two significant precipitation events in Canada: the three-day precipitation event that caused the June 2013 floods in Alberta (previously assessed by Teufel et al., 2017; Zhao et al., 2024) and the two-day precipitation event associated with the November 2021 floods in British Columbia (Gillett et al., 2022). Both floods were devastating in terms of human and financial losses, and both were classified as rain-on-snow events, where heavy rain was only a partial cause of the flooding. By analyzing extreme two-day precipitation spatially averaged over southwestern British Columbia, the latter event attribution study concluded that the probability of events like those associated with the 2021 floods in the region has significantly increased in today’s climate relative to the pre-industrial climate (1850 to 1900) and that there is a 45% greater chance (90% uncertainty range: 1−84%) of such events occurring now with human-induced climate change than without such climate change (Gillett et al., 2022).

8.3.1.2: Future changes

According to the results of multi-model ensemble simulations (downscaled and bias-adjusted Coupled Model Intercomparison Project Phase 6 [CMIP6] simulations), the magnitude of extreme one-day and five-day precipitation events in Canada will increase with future warming (Figure 8.10) (Sobie et al., 2024). Increases in annual maximum one-day and five-day precipitation amounts are projected across the country, with the greatest relative changes in the North. The spatial pattern is similar for one-day and five-day precipitation, with slightly greater relative changes in one-day precipitation. Increases in precipitation amounts are greater with larger global temperature increases.

Annual maximum one-day and five-day precipitation are projected to increase consistently with each increment of global warming in all regions (Figure 8.11) (see Figure 2.1 for a map of the regions). In the recent past (that is, 1°C of global warming above the pre-industrial level), annual maximum one-day precipitation averaged 23.9 mm for Canada as a whole. This value is projected to increase by 4.5% (80% uncertainty range: 2.3−6.4%) at 1.5°C of global warming, by 7.7% (80% uncertainty range: 5.2−10.4%) at 2°C of global warming, and by 20.7% (80% uncertainty range: 16.5−25.0%) at 4°C of global warming. Similarly, average annual maximum five-day precipitation averaged 46.9 mm for Canada as a whole in the recent past. It is projected to increase by 3.7% (80% uncertainty range: 1.7−5.8%) at 1.5°C of global warming, by 6.7% (80% uncertainty range: 4.1%−9.1%) at 2°C of global warming, and by 17.6% (80% uncertainty range: 13.7−22.8%) at 4°C of global warming. This overall pattern of intensification is expected to occur across all regions, but to be more muted in the Prairies (Figure 8.11), owing in part to circulation changes that weaken such events in the region (C. Li, Zwiers, Zhang, Chen, et al., 2019). Spatially, the pattern of intensification in the future is expected to be broadly consistent with the pattern of trends observed in the historical record (Figure 8.9). Unlike the historical record, however, decreases in annual maximum one-day and five-day precipitation intensity are not projected to occur in any regions under these levels of global warming.

Figure take-away: In the future, the heaviest precipitation events will become more intense.

Figure title: Projected changes in precipitation extremes across Canada

Figure 8.10: Maps showing values of a) annual maximum one-day and five-day precipitation in the recent past across Canada and future changes in annual maximum b) one-day and c) five-day precipitation. Projected changes relative to the recent past (that is, 1°C of global warming above the pre-industrial level), expressed as a percentage, are shown for three different global warming levels (1.5°C, 2°C, and 4°C above the pre-industrial level). See Chapter 3, Box 3.1, for an explanation of how global warming levels translate to increases in Canadian average temperatures over different time periods. The ensemble median is shown. Projections are based on CanDCS-M6, an ensemble of 26 downscaled (1/12°) and bias-corrected CMIP6 models. Data source: Sobie et al. (2024).

Figure take-away: Heavy precipitation extremes in all Canadian regions will become more intense with every increment of global warming.

Figure title: Projected regional changes in the intensity of precipitation extremes across Canada

Figure 8.11: Bar charts showing projected average Canada-wide and regional changes (expressed as a percentage) in annual maximum one-day and annual maximum five-day precipitation at different levels of global warming (1.5°C to 4°C above the pre-industrial level) relative to the recent past (that is, 1°C global warming above the pre-industrial level). The top of each bar shows the ensemble median, with the 80% uncertainty range of the ensemble results illustrated by the vertical black line. See Chapter 3, Box 3.1, for an explanation of how global warming levels translate to increases in Canadian average temperatures over different time periods. Projections were obtained with CanDCS-M6, an ensemble of 26 downscaled (1/12°) and bias-corrected CMIP6 models. Regional values and ranges can be found in the Supplementary Tables. Data source: Sobie et al. (2024).

Figure take-away: Heavy one-day rainfall extremes will occur more frequently in all Canadian regions with every increment of global warming, with greater changes for rarer events.

Figure title: Projected changes in the frequency of one-day precipitation extremes across Canada

Figure 8.12: Bar charts showing projected Canada-wide and regional changes in the frequency of the annual maximum one-day precipitation event that occurred, on average, once in 10, 20, and 50 years in the recent past (that is, 1°C of global warming above the pre-industrial level), and that is projected to occur under different global warming levels (1.5°C to 4°C above the pre-industrial level). Results are expressed as the average number of times an event of the given magnitude occurs during the designated time period at each global warming level. The different colours of the bars indicate events of different rarity in the recent past climate. See Chapter 3, Box 3.1, for an explanation of how global warming levels translate to increases in Canadian average temperatures over different time periods. The values were obtained from CanDCS-M6, an ensemble of 26 downscaled (1/12°) and bias-corrected CMIP6 models, and were computed using methods based on those in Kharin et al. (2018). Data source: Sobie et al. (2024).

Extreme one-day and five-day precipitation events are projected to become more frequent in Canada as the climate warms. A one-day precipitation event that occurred on average once in 10 years in Canada in the recent past is projected to occur on average 1.3 times (80% uncertainty range: 1.2−1.4 times) at 2°C of global warming and 2.1 times (80% uncertainty range: 1.6−2.3 times) at 4°C of global warming (Figure 8.12). Another way to consider the frequency of such events is in terms of the return period, or the average interval between occurrences of an event of a given magnitude. In this case, an event that occurs on average once every 10 years, known as a 10-year return period event, becomes a roughly 7-year event at 2°C of global warming and a roughly 4.5-year event at 4°C of global warming. In the case of a more extreme event, an event that occurred on average once every 50 years is projected to occur 1.4 times (80% uncertainty range: 1.3−1.5 times) at 2°C of global warming and 2.3 times (80% uncertainty range: 1.7−2.7 times) at 4°C of global warming. The increase in relative frequency is slightly greater for more extreme and rarer events. This finding is consistent with those for other global regions (C. Li et al., 2021b) and is supported by regional climate model simulations for North America (C. Li, Zwiers, Zhang, Chen, et al., 2019).

The frequency of extreme one-day precipitation events is projected to increase in all Canadian regions with every increment of global temperature increase (Figure 8.12). The largest increases in frequency are expected in Quebec and Atlantic Canada, while the smallest changes are projected for the Prairies. In all regions, rarer events are associated with greater relative changes in frequency. The increase in the frequency of extreme five-day precipitation events follows patterns that are very similar to those for one-day events, with slightly greater changes in Canada as a whole and in all regions. For example, an event that occurred on average once in 10 years in the recent past is projected to occur on average 1.4 times (80% uncertainty range: 1.3−1.5 times) at 2°C of global warming and 2.2 times (80% uncertainty range: 1.7−2.6 times) at 4°C of global warming. An event that occurred once in 50 years is projected to occur on average 1.5 times (80% uncertainty range: 1.3−1.6 times) at 2°C of global warming and 2.5 times (80% uncertainty range: 1.6−3.1 times) at 4°C of global warming. Greater changes in frequency are also projected for rarer five-day precipitation extremes in all regions.

Figure take-away: In the future, heavy precipitation events will occur more frequently.

Figure title: Projected changes in the number of heavy precipitation days (≥ 10 mm per day) across Canada

Figure 8.13: Maps showing a) the frequency (in number of days per year) of heavy precipitation days (defined as ≥ 10 mm of precipitation per day) in Canada in the recent past, with the number of days represented by different colours; and b) future changes in the frequency of these events, with the changes represented by different colours. In b), the projected changes relative to the recent past (that is, 1°C of global warming above the pre-industrial level) are shown for three different levels of global warming (1.5°C, 2°C, and 4°C above the pre-industrial level). See Chapter 3, Box 3.1, for an explanation of how global warming levels translate to increases in Canadian average temperatures over different time periods. Values represent the ensemble median. Projections are based on the results from CanDCS-M6, an ensemble of 26 downscaled (1/12°) and bias-corrected CMIP6 models. Data source: Sobie et al. (2024).

More frequent heavy precipitation days are also projected across Canada (Figure 8.13), with Canada’s North seeing the largest relative increase; this signal is consistent with the trends detected in the historical record (Figure 8.9c). In Canada’s North, an increase from 2.7 heavy precipitation days in the recent past to 3.3 heavy precipitation days (80% uncertainty range: 3.1−3.5 days) at 2°C of global warming is expected, representing a relative increase of more than 20%. At 4°C of global warming, this increases to 4.7 days (80% uncertainty range: 4.2 to 5.1 days), a relative increase of over 70%. In other regions, the corresponding relative increases at 4°C of global warming range from 17% in British Columbia to 32% in Quebec.

8.3.2: Short-duration rainfall extremes

Key Message 8.5: The intensity and frequency of short-duration (that is, timescales shorter than a day) rainfall extremes have increased for Canada as a whole (low confidence) since the mid-20th century. The lower confidence in shorter-duration precipitation extremes is due to greater spatial and temporal variability, the lower density of observing stations, and shorter length of records. Local and regional changes cannot be assessed with confidence for these same reasons.

Key Message 8.6: The intensity and frequency of short-duration rainfall extremes in Canada are projected to increase in the future (high confidence), with the increases becoming larger as global average temperature increases (high confidence). The projected rate of intensification of short-duration rainfall extremes for Canada as a whole is consistent with the rate of increase in atmospheric moisture along with average warming in Canada (medium confidence).

This section assesses past and projected changes in short-duration extreme rainfall events in Canada, specifically annual maximum rainfall intensities on timescales from minutes to 12 hours. Intense rain can cause flash floods and the failure of buildings, roads, bridges, and other infrastructure. Weather-related losses, primarily due to urban flooding in residential basements, accounted for more than Can$2 billion in annual insurable property damage claims on average in Canada for the 2009 to 2021 period, which is substantially more than the approximately Can$400 million in losses (normalized for inflation and per capita changes in wealth) recorded for 1983 to 2008 (Bakos, K. et al., 2022). The increase in losses is partly due to increased population exposure and urbanization, but rainfall-driven flooding is expected to increase in the future due to climate change (Chapter 5, section 5.7).

From a practical perspective, engineers use statistics that summarize the intensity, duration, and frequency of sub-hourly to multiple-hour rainfall extremes when designing buildings and infrastructure that meet specified levels of reliability (Canadian Standard Association, 2025). If structures are designed under the assumption that rainfall statistics will remain consistent over time, these structures may be compromised in the future if rainfall increases (Schlef et al., 2023).

8.3.2.1: Past changes and attribution

As described in Box 8.3, the laws of thermodynamics dictate that the atmosphere’s capacity to hold moisture increases by about 7% per 1°C increase in temperature. Globally, observations at stations with records from the 1979 to 2014 period (Lewis et al., 2019) indicate that extreme sub-daily rainfall has increased in parallel with the increase in atmospheric moisture due to warming (see figures S3 and S6 Ali et al., 2021). However, observations from monitoring stations in Canada, assessed in CCCR2019 based on the annual maximum rate-of-rainfall data available (Shephard et al., 2014), did not provide robust evidence of increasing trends in short-duration rainfall in Canada, specifically (X. Zhang et al., 2019). This was attributed to the large variability in (relative to the small thermodynamic signal), and shortness of, the rainfall records.

Canadian rate-of-rainfall records for sub-daily durations from 5 minutes to 12 hours (5 min, 10 min, 15 min, 30 min, 1 h, 2 h, 6 h, and 12 h durations) are now available to the end of 2021 (Cannon et al., 2024), while the assessment in CCCR2019 was limited to data to the end of 2005. The inclusion of 16 more years of observations from a period when Canada has warmed substantially—the three warmest years from 1948 to 2023 have occurred since 2006 (Chapter 2, section 2.4.1)—increases the statistical power of the results in terms of the trends detected and makes it easier to see the effects of the increased moisture in the air caused by warmer temperatures.

As noted for one-day and multi-day precipitation (section 8.3.1), the data are still insufficient to draw robust conclusions on the magnitude of trends at individual locations. Single-station trends are spatially noisy (Figure 8.14). However, a collective assessment of the results for all sub-daily durations across Canada reveals that significant increasing trends are present at roughly 2.5 times more locations than decreasing trends (Cannon et al., 2024). When the results are aggregated for Canada as a whole, significant increasing trends are detectable for six of the eight sub-daily durations analyzed, with none showing significant decreases. Furthermore, when data are aggregated for Canadian climate regions (originally defined by Gullett et al., 1992), approximately three quarters of regions show significant intensification for the 5 min to 2 h extremes, with median trend scaling (with dew point temperature) (Box 8.3) of about 5−9% per 1°C. This drops to approximately fewer than half of regions and fewer than 4% of regions per 1°C of warming for 6 h and 12 h events, respectively. The most consistent increases are found along the Pacific and Atlantic coasts, while signals are weaker in the southern interior of Canada. Overall, the evidence for an increasing trend in short-duration extreme rainfall is now stronger than in CCCR2019.

Figure take-away: More increases than decreases in short-duration extreme rainfall (sub-hourly to multiple hours) have been observed across Canada.

Figure title: Observed trends in short-duration rainfall extremes across Canada

Figure 8.14: Map showing the trends observed at monitoring stations across Canada during the 1950 to 2021 period in annual maximum values for a) 15 minute, b) 1 hour, and c) 6 hour rainfall (following Cannon et al., 2024). Trends were calculated as the change, expressed in millimetres per hour (mm/h), in the annual maximum 15 minute, 1 hour, or 6 hour rainfall intensity over this period at each station and are shown in the figure as the percentage (%) change per decade, relative to the average maximum rainfall intensity for each duration observed during the baseline period (1971 to 2000). The colour of the dots at each station location indicates the magnitude and direction of the trend. Trends that are significant at the 5% level (that is, ≤ 5% chance of concluding that an effect or trend exists when it does not) are shown with filled circles. Data source: Cannon et al. (2024).

8.3.2.2: Future changes

Many of the same physical processes that drive the intensification of one-day and multi-day precipitation extremes, such as thermodynamic influences on atmospheric moisture (Box 8.3), also play an important role in driving changes in sub-daily rainfall extremes. However, owing to their coarse spatial resolution, conventional global and regional climate models cannot explicitly model important small-scale physical phenomena, such as thunderstorms, that are responsible for producing intense local rainfall at shorter timescales (Fowler et al., 2021). Additionally, sub-hourly timescales may fall short of climate models’ time steps (Takayabu and Hibino, 2016; X. Zhang et al., 2017). Moreover, in simulations with very short time steps, sub-hourly precipitation is typically not archived consistently. Consequently, projections for these shortest rainfall durations cannot be assessed directly.

At hourly timescales, localized extreme rainfall can be projected using convection-permitting models, which are regional climate models with a sufficiently high spatial resolution (≤ 4 km grid spacing) to represent some convective processes explicitly (Fosser et al., 2024; Fowler et al., 2021; Scaff et al., 2020). However, these models are very expensive computationally and only allow short simulations (typically one to two decades), which may make it difficult to estimate rarer extremes (Cannon and Innocenti, 2019; C. Li, Zwiers, Zhang, and Li, 2019). Although these models have been used to run a small number of continental-scale simulations, the models’ high computational cost has tended to limit these to smaller study areas and a more limited set of experimental designs, as well as reducing the number of convection-permitting models that can be used.

In general, high-resolution regional climate models and convection-permitting simulations are in agreement that extreme hourly rainfall in Canada (1 h to 12 h durations) will intensify, with the rate of increase generally consistent with warming-driven increases in atmospheric moisture (Box 8.3) (Cannon and Innocenti, 2019; C. Li, Zwiers, Zhang, and Li, 2019; L. Li and Li, 2023; Pérez Bello et al., 2021; Prein et al., 2017; Teufel and Sushama, 2022). Given the projected warming of 2.7°C in Canada (90% uncertainty range: 1.8−3.6°C) in the near term (2021 to 2040) and of 5.0°C (90% uncertainty range: 3.8−6.6°C) in the late century (2081 to 2100) relative to the 1850 to 1900 period under the intermediate emissions scenario (SSP2-4.5) (Chapter 3, tables 3.1 and 3.2), increases of 20% (90% uncertainty range: 13−28%) and 40% (90% uncertainty range: 29−56%), in short-duration extreme rainfall amounts are projected in the near term and late century, respectively, based on Clausius-Clapeyron scaling (Box 8.3).

There is some evidence of regional differences in the scaling rate for rainfall intensification, with larger increases on the west and east coasts of Canada, and smaller increases in the continental interior (Cannon and Innocenti, 2019; C. Li, Zwiers, Zhang, and Li, 2019; L. Li and Li, 2023). In addition, for sub-daily extreme rainfall events in the same region, models typically project larger relative increases in intensity for shorter duration events than for longer duration ones and, for a given rainfall duration, the greater intensification of rarer, more extreme events, as compared to the more modest intensification of less rare extreme events. These regional, and duration- and rarity-dependent, differences have been attributed, in part, to changes in local atmospheric circulation (C. Li, Zwiers, Zhang, Chen, et al., 2019). Other factors, such as moisture availability, may also play a role (Pérez Bello et al., 2021).

8.3.3: One-day snowfall extremes

Key Message 8.7: The intensity and frequency of heavy one-day snowfalls have increased at most locations in northern Canada since the mid-20th century (medium confidence). Under future warming, these heavy snowfall amounts are projected to increase across much of northern and eastern Canada (medium confidence). In southwestern Canada, heavy one-day snowfall amounts have decreased since the mid-20th century (medium confidence), while no clear pattern has been observed in southeastern Canada. There is low confidence in the magnitude and direction of future changes in extreme snowfall events for southern Canada.

Nearly every location in Canada is affected by seasonal snowfall, whether due to sporadic storms or persistent accumulation during winter or over much of the year, as occurs in northern Canada. Extreme snowfall events can lead to avalanches in mountainous regions, transportation disruptions, and infrastructure damage (Hanesiak et al., 2022). Past and current data on snowfall and snow cover in Canada come from manual and automated observing sites, weather stations, satellite observations, and land surface models. Both observing sites and weather stations are located predominantly in southern Canada near population centres, with sparse coverage outside these regions. Satellite observations and land surface models provide additional data in remoter areas, yielding more complete information on the spatial extent of snow cover and on accumulated snowfall amounts when data from these sources are incorporated in reanalysis datasets (Mudryk et al., 2018). Owing to the different measurement methods used and data availability, in general, there is higher confidence in observed trends and projections in snow cover, which are assessed in Chapter 6, than in snowfall (Vincent et al., 2015). Chapter 2 (section 2.5.1.2) and Chapter 3 (section 3.5) assess past and future changes in average snowfall. In this section, we evaluate changes in extreme daily snowfall in Canada.

8.3.3.1: Past changes and attribution

Observations of extreme daily snowfall are often limited in Canada due to sparse station coverage and omissions caused by measurement gauges that do not report precipitation type or that underestimate amounts due to factors such as blowing snow (Chapter 2, section 2.3.4.2). Long-term trends in extreme daily snowfall have therefore been assessed using proxy snowfall data, calculated from daily precipitation and daily average temperature data (Chapter 2, section 2.5.1.2, Box 2.3) (X. L. Wang and Feng, 2026). On the basis of the available station data for 1949 to 2023, a decreasing trend can generally be observed in the highest annual one-day snowfall amounts at most sites in southwestern Canada, while an increasing trend can be observed at many sites in northern Canada (Figure 8.15a). The number of heavy snowfall days (days with snowfall ≥ 10 mm snow water equivalent) has also decreased during the 1949 to 2023 period at most locations in southern Canada, but has increased in northern Canada (Figure 8.15b). Previous analyses based on in situ daily snowfall measurements (Vincent et al., 2018; X. Zhang et al., 2019) detected more variable trends in extreme daily snowfall, with fewer locations displaying substantial changes. In many instances, nearby stations even exhibited trends in the opposite direction (for example, negative versus positive), highlighting the high spatial variability in extreme snowfall across Canada (Figure 8.15).

Figure take-away: Since the mid-20th century, the maximum one-day snowfall amount and frequency of heavy snowfall days have mostly decreased in southern Canada and mostly increased in far northern Canada.

Figure title: Trends in observed snowfall extremes across Canada

Figure 8.15: Maps showing trends, expressed as the percent change per decade, in a) the maximum one-day snowfall amount and b) the number of heavy snowfall days (defined as days with more than 10 mm of snowfall) at monitoring stations across Canada during the 1949 to 2023 period. The solid dots indicate stations with trends significant at the 5% level (meaning that there is ≤ a 5% chance of concluding that an effect or trend exists when it does not). The snowfall indices were assessed using proxy snowfall data calculated from daily precipitation and daily average surface air temperature, with snow assumed to occur whenever the latter was below a station-specific threshold, which mostly ranges from -2°C to 2°C (Chapter 2, box 2.3). Data sources: X. L. Wang and Feng (2026), Qian et al. (2025)

Average and extreme daily snowfall are expected to respond differently to warming, based on the understanding of physical processes (O’Gorman, 2014). Over land and near the ground, snowfall occurs at a fairly wide range of temperatures ranging up to roughly 4°C and can occur at even higher temperatures in low-humidity conditions (Dai, 2008; Thériault et al., 2018). In contrast, extreme snowfall occurs most frequently at temperatures slightly below freezing, within a fairly narrow range around -2°C (O’Gorman, 2014). Therefore, while average daily snowfall is expected to decrease in response to warming (Chapter 3, section 3.5), extreme snowfall may still occur under warming provided that temperatures reach the optimal range of around -2°C. The varied pattern of positive and negative trends in heavy snowfall shown in Figure 8.15 in the southern parts of central and eastern Canada may be an indication of this, compared to the more uniform pattern of decreasing trends seen in average daily snowfall (Chapter 2, section 2.5.1.2). However, the large decreases in annual maximum one-day snowfall amounts seen on Canada’s west coast are consistent with the expected decline in this metric in a more temperate climate (O’Gorman, 2014).

8.3.3.2: Future changes

With additional warming in Canada projected under all emissions scenarios, annual total snowfall is expected to decrease in the southernmost parts of Canada, particularly in coastal regions, and to increase in northern Canada (Chapter 3, section 3.5). As mentioned above, warming is expected to have a somewhat different effect on extreme snowfall than on average snowfall. Indeed, some climate model studies have projected increases in extreme snowfall amounts in much of Canada, even in regions where such extreme events are expected to become less frequent (McCray et al., 2023; Quante et al., 2021). There is little agreement among models on the direction of change in extreme one-day snowfall until a global warming level of around 2°C is reached, at which time most of eastern Canada and some areas of northern Canada are projected to have robust increases of 5−15% relative to the recent past under 1°C of warming (Figure 8.16a−d). By the time a warming level of 4°C is reached, nearly all of Canada displays increases, most in the 5−20% range (Figure 8.16e). The regions projected to experience increases in heavy snowfall have cold season temperatures that are expected to warm toward the near‐zero values optimal for such events (Chapter 3, section 3.4), while still remaining below freezing. Projected increases in atmospheric moisture and in both average and extreme precipitation in winter across most of the country (Chapter 3, section 3.5; section 8.3.1.2) are key to these projected increases in extreme snowfall.

Projections of substantial decreases in annual maximum snowfall are limited to small areas of coastal British Columbia, Nova Scotia, and southern Ontario, where average winter nighttime low temperatures are projected to increase above the optimal range for heavy snowfall (Chapter 3, section 3.5). Decreases in extreme snowfall in coastal areas projected by models should be interpreted with caution, however, due to meteorological conditions that lead to rare, but sometimes heavy, snowfalls in those regions. These events often result from the collision of frigid Arctic air and warm, moist flows off the Pacific and Atlantic oceans (Geng et al., 2012; Wu et al., 2013). Determining where and when heavy snowfall will occur is a challenging task for meteorologists using state‐of‐the‐art high‐resolution weather forecasting models, meaning that there is limited confidence in the ability of CMIP‐class Earth system models used for climate projections to capture this phenomenon accurately (McCray et al., 2023; Zarzycki, 2018).

Figure take-away: Extreme one‐day snowfall amounts are projected to increase in most areas of Canada with increasing global warming, except along the east and west coasts and in southern Ontario.

Figure title: Projected changes in snowfall extremes

Figure 8.16: Trends in annual maximum one‐day snowfall for Canada in the recent past (that is, 1°C of global warming above the pre-industrial level) and in the future at three different levels of global warming (1.5°C, 2°C, and 4°C of warming above the pre-industrial level). a) Past and future changes in annual maximum one‐day snowfall for Canada as a whole over the 1950 to 2100 period, using historical and future simulations by CanDCS-M6 (grey curves), an ensemble of 26 downscaled (1/12°) and bias-corrected CMIP6 models, with projections based on the very high emissions scenario (SSP5‐8.5); the heavy blue curve shows the median value of the model ensemble in this time series. b) Annual maximum one‐day snowfall during the recent past (that is, 1.0°C of global warming) based on the model ensemble median. c), d), and e) Relative changes in annual maximum one-day snowfall (in %) at 1.5°C, 2°C, and 4°C of global warming above the pre‐industrial level; diagonal hatching indicates regions with low model agreement (that is, where 20% or more of models disagree about the direction of the projected changes from the multi‐model median). See Chapter 3, Box 3.1, for an explanation of how global warming levels translate to increases in Canadian average temperatures over different time periods. Snowfall amounts were calculated as a proxy using simulations of precipitation that are partitioned into rainfall and snowfall using a temperature‐index based method (Dai, 2008) and reported in mm of snow water equivalent. The Dai method apportions all precipitation to snowfall below about -2°C and all to rainfall above about 5°C, with a smoothly varying fractional allocation to each between those temperatures. Data source: Sobie et al. (2024).

In summary, station observations and snowfall proxy data since the mid-20th century indicate a decrease in the frequency of extreme snowfall events in most parts of southern Canada and an increased frequency in most parts of northern Canada. Heavy one-day snowfall amounts have decreased in southwestern Canada and increased in the Far North, while changes in this metric in the remainder of the country are variable, with no clear pattern. This contrasts somewhat with the more uniform trend of declining daily total snowfall for southern Canada (also inferred from snowfall proxy data; Chapter 2, section 2.5.1.2), but is consistent with the trends observed in the peak annual snowpack, as well as the smaller expected changes in extreme snowfall compared to average snowfall under the same amount of warming. Moreover, owing to projected increases in atmospheric moisture and both average and extreme precipitation in winter for most of the country (Chapter 3, section 3.5; section 8.3.1.2), maximum one-day snowfall amounts are expected to increase in areas where freezing winter temperatures remain prevalent. Overall, model projections indicate that extreme one-day snowfall amounts will increase over most of Canada, especially in northern and eastern regions at higher global warming levels. In contrast, there is low model agreement on the magnitude and direction of change in extreme snowfall amounts under future warming for much of southern Canada.

8.3.4: Freezing rain extremes

Key Message 8.8: There is insufficient evidence to assess historical changes in extreme freezing rain events—those that cause an accumulation of ice sufficient to disrupt infrastructure—in Canada, due to limited observational data and a paucity of studies focusing on historical trends. The frequency of extreme freezing rain is projected to increase in most regions of Canada (low confidence), while some decreases or no significant changes are projected in parts of southern Ontario, the Atlantic provinces, and coastal regions of Hudson Bay (low confidence).

Only extreme freezing rain events are assessed in this section. Historical trends and future changes in moderate freezing rain events are assessed in Chapter 2, section 2.5.1.3, and Chapter 3, section 3.5, respectively. Extreme freezing rain is typically defined as an event that results in the accumulation of ice on structures and on the ground that is thick enough to disrupt infrastructure, by damaging power lines and causing outages, breaking tree limbs, disrupting road and air transportation, collapsing roofs under ice loads, creating hazardous walking conditions, and other things. The Canadian Standards Association (Canadian Standards Association, 2010) uses 50-year return levels of annual maximum ice thickness for designing overhead transmission lines. The term “return level” refers to the specific magnitude of extreme events that are statistically expected to occur, on average, once every defined return period (for example, 50 years). CSA (Canadian Standard Association, 2014) also applies 20-year return levels for highway bridge design. Other impact-based definitions of extreme freezing rain, such as a 12.5-mm threshold (Chartrand et al., 2023; Klaassen et al., 2003), depend on the severity of the impacts, including widespread transportation disruptions, property damage, or risks to human safety. Similarly, a severe icing event is defined as a compound event involving freezing rain and wind, where the wind-induced force on a 25 mm diameter cable exceeds 10 newtons per metre (N/m) (Henson and Stewart, 2007).

Extreme freezing rain can cause significant natural disturbances that impact various sectors in Canada, including wind energy generation, urban infrastructure, communication systems, forestry operations, and electrical networks. For example, the 1998 ice storm (January 1998) in eastern Canada and the northeastern United States resulted in extreme freezing rain, leading to over 100 mm of ice accumulation on structures in southern Quebec (Roebber and Gyakum, 2003). This severe event claimed approximately 47 lives, toppled 1000 transmission towers, damaged 30,000 utility poles, and felled millions of trees. It also caused significant disruptions to farms and transportation systems across eastern Canada and the northeastern United States (Henson et al., 2007). The 2023 extreme freezing rain event in Montréal resulted in approximately 40 mm of ice accumulation in a single day, with severe adverse impacts on urban functioning (Theriault et al., 2024). Extreme freezing rain events in eastern Canada are generally larger in magnitude than those in western and central Canada (Jeong et al., 2018, 2019) (Figure 8.17), which is consistent with the annual total freezing rain amounts in these regions (Cardinal et al., 2024; McCray et al., 2019).

8.3.4.1: Past changes and attribution

Measuring freezing rain at observing stations and simulating it in climate models are challenging because freezing rain is often mixed with other types of precipitation, such as snow and ice pellets, which are formed under similar atmospheric conditions (R. E. Stewart et al., 2015). Freezing rain also occurs less frequently than rain or snow, due to the narrow range of air temperatures required, both aloft and at the surface (Bresson et al., 2017; Cannon et al., 2020; Thériault et al., 2010);. Ice storms—extreme weather events involving prolonged freezing rain and significant ice accumulation—are among the most difficult extreme events to detect and determine trends in, partly because of their complex variability and our limited understanding of the potential physical causes of these events (Kunkel et al., 2013). These challenges could explain the paucity of studies on historical changes in the frequency and intensity of extreme freezing rain events and their attribution.

Jeong et al. (2019) examined the climatology of freezing rain extremes across North America, including Canada, and estimated the 20-year return level for annual maximum daily freezing rain during the 1986 to 2016 period. Their analysis was based on observations from 383 stations across Canada, as well as the North American Regional Reanalysis (NARR) dataset (Figure 8.17a). A similar spatial pattern, but with larger magnitudes due to the longer return period, is also evident in the 50-year return levels in Canada for the 1986 to 2016 period derived from the NARR dataset (Jeong et al., 2018) (Figure 8.17b). These findings indicate that NARR data reasonably reproduce the spatial pattern of extreme freezing rain events, providing confidence in its applicability in climate assessments in Canada. The spatial pattern for the 20-year return level shows greater magnitudes in eastern Canada than in western and central Canada, consistent with the pattern observed for annual average freezing rain amounts, as reported by Cardinal et al. (2024) and McCray et al. (2019).

Several studies have shown that the occurrence of freezing rain is primarily driven by humid air conditions with favourable vertical air temperature profiles from aloft to the surface—specifically a warm layer aloft (with temperatures above 0°C), where precipitation melts, and a shallow sub-freezing layer near 0°C at the surface (for example, Jeong et al., 2019; McCray et al., 2022; Stewart et al., 2015; Mekis et al., 2020). Research has not yet been conducted on the effects of changes in vertical temperature profiles and other contributing factors (for example, precipitation duration, rate, and type; and wind strength) that affect the frequency of extreme freezing precipitation.

Figure take-away: Extreme freezing rain events have been more intense in eastern Canada than in western and central Canada.

Figure title: Observed and modelled extreme freezing rain across North America

Figure 8.17: Maps of North America showing 20-year return levels and 50-year return levels of daily maximum freezing rain (in mm). a) 20-year return levels based on station observations (obtained from 205 meteorological stations across Canada operated by Environment and Climate Change Canada and 383 stations across the United States operated by the National Climatic Data Center) and the North American Regional Reanalysis (NARR) dataset for the 1986-2016 period. b) 50-year return levels based on the NARR dataset for the 1979 to 2005 period. Adapted from: Jeong et al. (2019).

8.3.4.2: Future changes

Only a few climate model studies have projected changes in extreme freezing rain events and the resulting ice accumulation on structures and the ground in Canada. Two studies are discussed here. In the first study, Jeong et al. (2018) used a regional climate model to simulate projected changes in the 50-year return level of annual maximum daily freezing rain events in North America for the 2071 to 2100 period relative to the 1976 to 2005 period (Figure 8.18a). Different combinations of global climate models and emissions scenarios (including the intermediate representative concentration pathway 4.5 [RCP4.5] emissions scenario and the very high [RCP8.5] emissions scenarios) were used to run the regional climate model simulations. All three simulations projected an overall increase in extreme freezing rain in eastern Canada and scattered increases in western and central Canada, with generally greater changes under the very high emissions scenario. The projections also indicated decreases or no significant changes in extreme freezing rain events in southern Ontario, the Maritime provinces, and coastal regions of Hudson Bay, with more pronounced decreases under the very high emissions scenario. In the second study, Jeong et al. (2019) used a regional climate model to produce an ensemble of 50 simulations with different initial conditions to project the 50-year return levels of annual maximum freezing rain events across North America (Figure 8.18b). A very high emissions scenario (RCP8.5) was assumed in the simulations, but results were reported for global warming levels of approximately 2°C, 3°C, and 4°C relative to the pre-industrial period. This study also projected significant increases in extreme freezing rain events in many regions of Canada, but decreases or no significant changes in southern Ontario, the Atlantic provinces, and the coastal regions of Hudson Bay. These projected increasing and decreasing trends in extreme freezing rain are particularly evident at higher global warming levels. Western and central Canada had larger relative increases (expressed as a percentage), while eastern Canada generally had larger absolute increases. The spatial distribution of the projected changes in extreme freezing rain is consistent with that for annual hours of freezing rain (Chapter 3, Figure 3.15), highlighting the robustness of these patterns across various freezing rain indices.

Figure take-away: Projected changes in extreme freezing rain suggest increases in many regions of Canada, but decreases or no significant change in southern Ontario, the Maritime provinces, and the coastal regions of Hudson Bay.

Figure title: Projected changes in freezing rain extremes (50-year return level) across North America

Figure 8.18: Maps of North America showing projected changes in the 50-year return level of annual maximum daily freezing rain. a) Projected changes for 2071 to 2100, relative to 1976 to 2005, in mm of freezing rain, generated by the CRCM5 regional model driven by the CanESM2 global model under an intermediate emissions scenario (RCP4.5), the CanESM2 model under a very high emissions scenario (RCP8.5), and the MPI-ESM global model under an intermediate emissions scenario (RCP4.5), respectively. b) Projected changes at the global warming levels of 1°C, 2°C, and 3°C relative to the 1986 to 2016 period, corresponding approximately to global warming levels of 2°C, 3°C, and 4°C from the pre-industrial era (1850 to 1900), from the ensemble average of the CanRCM4 regional model, driven by 50 initial-condition CanESM2 simulations under the very high emissions (RCP8.5) scenario. See Chapter 3, Box 3.1, for an explanation of how global warming levels from the pre-industrial era translate to increases in Canadian average temperatures over different time periods. A freezing rain event is considered to be a separate event if the surface temperature rises above 1°C or if seven days have passed without further freezing rain. Adapted from: a) Jeong et al. (2018) and b) Jeong et al. (2019).

Chapter 12 of the IPCC AR6 WGI assessment expressed low confidence in the projected changes in the timing, severity, and occurrence of ice storms (Ranasinghe et al., 2021), which implies a similar level of uncertainty regarding extreme freezing rain events. This uncertainty applies to projections by both global and regional climate models, as climate model projections of hazardous precipitation types such as freezing rain, ice pellets, and wet snow are made more difficult by the fact that these precipitation types occur when air temperatures are near 0°C aloft as well as at the surface (R. E. Stewart et al., 2015). Similarly, the assessment of future climatic design variables for Canada concluded that there is low confidence in projections of extreme freezing rain and associated extreme ice thickness (Cannon et al., 2020). This uncertainty is attributed to factors such as temperature and precipitation biases in climate models (for example, Chapter 3, section 3.3.2), limitations in representing precipitation processes, and discrepancies between the model and actual topography. Topographic discrepancies can affect the accurate representation of local patterns of extreme freezing rain, due to the simplification of complex terrain features such as elevation changes, mountain ranges, and valleys.

8.3.5: Hail

Key Message 8.9: There is insufficient evidence to assess historical changes in severe hail (≥ 2 cm in diameter) for Canada as a whole. Limited studies examining trends in hail of any diameter suggest regionally contrasting patterns, with increasing trends in Alberta and decreasing trends in Saskatchewan and Manitoba over recent decades (very low confidence). Projections indicate a potential increase in the frequency of severe hail across parts of western Canada (very low confidence), but this assessment is based on limited evidence and involves substantial modelling challenges.

Hail is a type of solid precipitation, with a diameter of at least 5 mm, that typically takes the form of balls of ice or irregular clusters of smaller hailstones. Hail forms in thunderstorms through the combined effect of multiple complex processes. These include updrafts that are strong and long-lasting enough to loft raindrops to altitudes well above the freezing level and to produce hailstones large enough so that they do not melt. Updraft strength is governed primarily by the amount of convective instability, while updraft duration is strongly modulated by the amount of vertical wind shear near the thunderstorm. In a changing climate, however, the complex interplay of these factors makes future changes in hail size difficult to predict. The impact of hail is influenced by its size, density, and total amount (large accumulations of hail of any size can damage crops or block drainage and cause flash floods), and whether it occurs in combination with strong winds, as this increases the momentum of a hailstone (J. T. Allen et al., 2020; Brimelow, 2018). In this section, only severe hail (≥ 2 cm in diameter) is assessed.

8.3.5.1: Past changes and attribution

According to observational studies, severe hail falls mostly in the parts of southern Canada east of the Rocky Mountains and west of the Maritime provinces (Battaglioli et al., 2023; Brunet and Brimelow, 2024; Raupach et al., 2021). This pattern is also found in the reported occurrence of hail of any diameter (Etkin and Brun, 1999). Extreme hail (a subset of severe hail with a diameter of 7 cm or greater) has been recorded in a number of Canadian provinces, usually far from the coasts, but its frequency of occurrence is not yet known. Canada’s largest hailstones are typically recorded in the area from the southern Prairie provinces east to southern Ontario (Brunet and Brimelow, 2024).

During the late 20th century, researchers started to detect a significant trend in the occurrence of hail in Alberta, where hail frequency was found to increase after 1982; no significant trends were detected elsewhere in Canada (Etkin and Brun, 1999). However, the increase in Alberta may be partly attributable to improvements in hail reporting, specifically more frequent reporting. In an assessment of hail trends in North America from 1950 to 2021, modest but statistically significant decreases in severe hail were found during summer in Ontario and Quebec, while modest but statistically significant increases were found in parts of the Prairie provinces (Battaglioli et al., 2023). Southern Alberta had a larger, statistically significant increase, particularly for hail between 2 cm and 5 cm in diameter. It should also be noted that no significant national-scale hail trends have been detected in the United States (Raupach et al., 2021).

8.3.5.2: Future changes

Only one study has directly modelled future hail occurrence in an area that includes Canada. J.C. Brimelow et al. (2017) examined differences between past (1971−2000) and projected future (2041−2070) hail frequencies and sizes in North America. The authors employed a one-dimensional hail model (HAILCAST) driven by 50 km regional climate simulations using three different NARCCAP modelling systems (Mearns et al., 2012) under a very high emissions scenario (SRES A2^{Footnote 5}).

The results suggest that most regions in Canada will experience a similar or reduced number of hail days during the summer. However, areas of Alberta and British Columbia near the Rocky Mountains (including Calgary and Edmonton) are projected to experience a notable increase in the number of hail days per season for all defined hail diameter categories (at least 1 cm, at least 2 cm and at least 4 cm) (see Figure 8.19 for projections of hail with a diameter of at least 2 cm during the summer season), as well as an increase in potential hail damage. This is because, according to these authors, cooler and drier regions will experience the greatest increase in hail days, while warm and moist regions are more likely to see a decrease in hail days. Specifically, in the current climate, the potential for strong updrafts is typically limited by the amount of near-surface moisture. The anticipated rise in low-level moisture in the future climate is expected to generally increase convective instability in cooler and drier areas. In contrast, in regions currently characterized as warm and humid (that is, the Great Lakes and the southeastern Prairies in Canada), a projected rise in the melting level height in the atmosphere is expected to reduce (and in some cases offset) any increase in hail size resulting from additional convective instability.

A second study attempted to assess the effects of climate change on severe thunderstorm events in a more general sense, examining future changes in convective instability and vertical wind shear using 45km Canadian Regional Climate Model (CRCM) simulations under the intermediate (SRES A1B) and very high (SRES A2) emissions scenarios (Paquin et al., 2014). According to the projections, convective instability would increase, while wind shear would decrease slightly, resulting in an overall increase in the frequency of events with at least 25 mm/day of convective precipitation (precipitation caused by convection). There were no explicit results for hail.

Figure take-away: Some areas of Alberta and British Columbia near the Rocky Mountains are projected to experience an increase in severe hail days in summer.

Figure title: Projected change in days with severe hail

Figure 8.19: Map showing projected changes in the number of severe hail days (hail with a diameter of 2 cm or larger) in summer across most of North America. The changes shown (future minus present) represent the average number of severe hail days per summer (June-August) during the 2041 to 2070 period compared to the baseline period (1971 to 2000) under a very high emissions scenario (SRES A2). Coloured grid cells indicate changes where all three modelling systems agreed on the direction of change; circle-shaped cells indicate where changes from at least two of those modelling systems were statistically significant at the 10% level (that is, there is ≤ a 10% chance of concluding that an effect or trend exists when it does not). The change values represent the mean from all three modelling systems. The bold black line indicates the boundary of the study area. Adapted from: Brimelow et al. (2017).

8.3.6: Confidence terms in key messages: summary of evidence

Key Message 8.3: The intensity and frequency of one-day and five-day precipitation extremes have increased in Canada as a whole since the mid-20th century (medium confidence), consistent with an increase in atmospheric moisture across Canada due to warming. Human influence on the climate is the main driver of the observed intensification of extreme precipitation at the continental scale across North America (high confidence). Increases in the intensity and frequency of one-day and five-day precipitation extremes have been observed with low confidence in many, but not all, regions of Canada since the mid-20th century. These regional changes are uncertain, due to large spatial and temporal variability.

Key Message 8.4: Increases in the frequency and intensity of one-day and five-day precipitation extremes are projected for all regions of Canada (high confidence), with changes becoming larger as global average temperature increases (high confidence).

Key Message 8.5: The intensity and frequency of short-duration (that is, timescales shorter than a day) rainfall extremes have increased for Canada as a whole (low confidence) since the mid-20th century. The lower confidence in shorter-duration precipitation extremes is due to greater spatial and temporal variability, the lower density of observing stations, and shorter length of records. Local and regional changes cannot be assessed with confidence for these same reasons.

Key Message 8.6: The intensity and frequency of short-duration rainfall extremes in Canada are projected to increase in the future (high confidence), with the increases becoming larger as global average temperature increases (high confidence). The projected rate of intensification of short-duration rainfall extremes for Canada as a whole is consistent with the rate of increase in atmospheric moisture along with average warming in Canada (medium confidence).

Key Message 8.7: The intensity and frequency of heavy one-day snowfalls have increased at most locations in northern Canada since the mid-20th century (medium confidence). Under future warming, these heavy snowfall amounts are projected to increase across much of northern and eastern Canada (medium confidence). In southwestern Canada, heavy one-day snowfall amounts have decreased since the mid-20th century (medium confidence), while no clear pattern has been observed in southeastern Canada. There is low confidence in the magnitude and direction of future changes in extreme snowfall events for southern Canada.

Key Message 8.8: There is insufficient evidence to assess historical changes in extreme freezing rain events—those that cause an accumulation of ice sufficient to disrupt infrastructure—in Canada, due to limited observational data and a paucity of studies focusing on historical trends. The frequency of extreme freezing rain is projected to increase in most regions of Canada (low confidence), while some decreases or no significant changes are projected in parts of southern Ontario, the Atlantic provinces, and coastal regions of Hudson Bay (low confidence).

Key Message 8.9: There is insufficient evidence to assess historical changes in severe hail (≥ 2 cm in diameter) for Canada as a whole. Limited studies examining trends in hail of any diameter suggest regionally contrasting patterns, with increasing trends in Alberta and decreasing trends in Saskatchewan and Manitoba over recent decades (very low confidence). Projections indicate a potential increase in the frequency of severe hail across parts of western Canada (very low confidence), but this assessment is based on limited evidence and involves substantial modelling challenges.

Consistent with the expected increase in atmospheric moisture due to warming, and in line with findings for other regions of North America, studies reporting trends in observed precipitation since the mid‑20th century provide medium confidence that the frequency and intensity of one-day and five-day precipitation extremes have increased in Canada as a whole. Confidence is lower for precipitation than for temperature, because observed extreme precipitation trends show higher spatial and temporal variability and regional-scale assessments remain limited or inconsistent due to sparse observations; this also leads to low confidence in historical changes at smaller scales. Attribution studies provide robust evidence that observed increases in daily extreme precipitation across North America and larger spatial domains are mainly driven by human influence, supporting high confidence in the national-scale assessment. For the future, multiple lines of evidence, including consistent projections of increases in precipitation extremes by global and regional climate models, process-based understanding of how warming increases atmospheric moisture, and the observed and projected intensification of meteorological drivers such as atmospheric rivers (Chapter 4, section 4.5), converge to provide high confidence that extreme one-day and five-day precipitation in Canada will increase with additional global warming, with the magnitude of these increases becoming larger at higher global temperature levels due to the approximately exponential increase in atmospheric moisture with temperature (Box 8.3).

Updated observational data provide some evidence that the intensity of short-duration extreme rainfall (at timescales of less than a day) has increased in Canada as a whole since the mid-20th century (low confidence). The low confidence in the observed changes is due to the large spatial and temporal variability in the data and the low density of observation stations. Robust conclusions cannot be drawn on the magnitude of the trends at local and regional scales. However, on the basis of projections by regional climate models and a limited number of convection-permitting simulations, combined with the understanding of the physical processes that lead to short-duration extreme rainfall, there is high confidence that the intensity of sub-daily extreme rainfall in Canada will increase in the future, with increases becoming greater as the global average temperature rises. While physical process understanding increases confidence in the intensification of extremes over shorter durations compared to longer (that is, one-day and five-day) ones, model limitations reduce confidence in projections of the magnitude of these changes. The scaling of short-duration extreme rainfall with temperature (Box 8.3) appears to be roughly consistent with the theoretical Clausius-Clapeyron relationship (medium confidence). However, there is limited evidence for potential deviations from this rate—including those involving the region (that is, higher along the coasts and lower in continental interior areas), duration (that is, higher for shorter durations), and the rarity of the event (that is, higher for events with a longer return period)—albeit with large uncertainty in the details at this time.

There is medium confidence that the intensity and frequency of heavy one-day snowfall events have increased at most locations in northern Canada since the mid-20th century, but have decreased in southwestern Canada and show no consistent pattern in the rest of southern Canada. Confidence in the observed changes is limited by sparse station coverage, measurement uncertainty caused by factors such as blowing snow, high variability in extreme snowfall at Canadian sites (including sites located close to one another), and reliance on snowfall proxies. Model projections and the understanding of physical processes support medium confidence in continued increases in heavy one-day snowfall amounts in areas of northern and eastern Canada where freezing winter temperatures will remain prevalent under further warming, and low confidence in the magnitude and direction of the changes in most of southern Canada.

Evidence of observed changes in extreme freezing rain events in Canada is limited, due to sparse observational data and the small number of focused studies. Although some research suggests that these events were greater in magnitude in eastern Canada than in western and central Canada, assessing the overall historical trends and their attribution to anthropogenic and natural causes remains challenging at this time. Some studies project an increase in the frequency of extreme freezing rain events in most of Canada in the future, particularly under higher global warming scenarios, while others project decreases or no significant changes in the Atlantic provinces, parts of southern Ontario, and coastal regions of Hudson Bay. However, there is low confidence in these projections due to the high spatial variability and the complexity of accurately modelling freezing rain, which only occurs under specific atmospheric conditions aloft and at the surface. Temperature- and precipitation-related biases in climate models introduce further uncertainty in the projections.

There is insufficient evidence to assess historical changes in severe (≥ 2 cm in diameter) hail events in Canada as a whole, due to the scarcity of observational data and the paucity of focused studies. However, an analysis of trends for hail of any diameter has found increasing trends in Alberta and decreasing trends in Saskatchewan and Manitoba over the past few decades, although these trends are assessed as having very low confidence due to the limited observational data and the small number of focused studies. One study projects an increase in severe hail in parts of western Canada, but there is very low confidence in this result, given that there are no corroborating studies.

8.4: Wind extremes

Key Message 8.10: There is insufficient evidence to assess historical changes in extreme winds in Canada due to limited station coverage and representativeness, significant uncertainties in reanalysis products, and the paucity of studies examining trends in wind extremes. There is very low confidence in the direction and magnitude of future changes in extreme winds due to the limited number of studies and the substantial uncertainty related to model projections of wind-related phenomena.

Extreme winds are defined as events that are rare for a specific location, based on observed or simulated wind speeds near the surface, typically at a height of 10 m. In Canada, these extreme wind speeds are usually associated with storms such as extratropical cyclones (assessed in Chapter 4, section 4.4), atmospheric rivers (assessed in Chapter 4, section 4.5), North Atlantic hurricanes (assessed in Chapter 4, section 4.6), and severe thunderstorms (not assessed, although changes in large-scale environmental conditions favouring thunderstorms are assessed in Chapter 4, section 4.7) (Booth et al., 2015; T. Chen and Di Luca, 2025; Gentile et al., 2023; Letson et al., 2021; Parton et al., 2010; Seneviratne et al., 2021). In this section, we assess past and projected changes in wind extremes and their relationships with some key drivers, such as North Atlantic hurricanes and extratropical cyclones, as well as thunderstorms.

8.4.1: Past changes and attribution

Only a handful of studies have estimated trends in wind extremes in Canada. Most of the trends detected are inconsistent from season to season and region to region, and are heavily influenced by the period and dataset considered (Hundecha et al., 2008; Kumar et al., 2015; S. Li et al., 2017). An analysis of extreme wind speed data obtained from weather station observations spanning at least 30 years revealed non-statistically significant trends for most stations in Canada (12 out of 20 stations), significant decreases for some stations (6 stations), and significant increases for a very small number of stations (2 stations) (S. Li et al., 2017). When reanalysis datasets are used to evaluate historical trends in extreme wind speeds, the results are not consistent across space or over time (Kumar et al., 2015). Global multi-platform satellite data show increases in wind extremes (90th percentile) across the North Atlantic and Pacific oceans from 1985 to 2018 (Ribal and Young, 2019; Young and Ribal, 2019). For North America as a whole, Ranasinghe et al. (2021) assessed that there is limited evidence of, and low agreement in, past changes in severe wind speeds associated with extratropical cyclones and severe thunderstorms.

8.4.2: Future changes

A limited number of studies have examined projected changes in extreme wind speeds (C. S. Cheng, 2014; Jeong and Sushama, 2019; Kumar et al., 2015; Morris and Kushner, 2025; Morris et al., 2024). Projected changes in extreme wind events—those expected to happen only once every 50 years—for the 2071 to 2100 period relative to the 1981 to 2010 baseline period are generally modest (within 10% of the present-day value) and not statistically significant under the intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios (Jeong and Sushama, 2019). Projected changes in the magnitude of extreme wind events show inconsistent signals in different models and scenarios, with both increases and decreases projected depending on the experimental setup (Jeong and Sushama, 2019; Kumar et al., 2015). This dependence on the model and scenario used contributes to the very low confidence in the direction of future changes. Recent studies by Morris et al. (2024) and Morris and Kushner (2025) have shown that, under the very high emissions scenario (RCP8.5), even the direction of change in wind extremes differs depending on the model resolution. Overall, climate model projections point to small and mostly non-statistically significant changes in extreme wind speeds in most of Canada, leading to very low confidence in both the direction and magnitude of these projected changes.

8.4.3: Winds from extratropical cyclones and North Atlantic hurricanes

Most regions in Canada are affected by extratropical cyclones (storms that develop outside tropical regions). These systems occur year-round, but are generally more frequent and intense during winter and the shoulder seasons (fall and spring), when the temperature difference between the Equator and the North Pole is the greatest (Booth et al., 2015; T. Chen et al., 2022; Letson et al., 2021; Seiler and Zwiers, 2016a). In line with this, extreme winds occur most frequently in coastal and offshore regions in winter, and in inland regions in spring and fall, although some inland areas also experience peaks in winter and summer (Booth et al., 2015; T. Chen and Di Luca, 2025; T. Chen et al., 2022; Letson et al., 2021). Figure 8.20 shows that, in all seasons, a large proportion of extreme winds are linked to extratropical cyclones and North Atlantic hurricanes, particularly in central and eastern Canada. In contrast, in western Canada, many wind extremes are linked to atmospheric rivers, which can account for more than 70% of such events (Chapter 4, Figure 4.11).

Some regions, especially Atlantic Canada, are also affected by North Atlantic hurricanes, which can sometimes travel far enough to impact Canada. Many such hurricanes transition into what are termed “post-tropical cyclones” before reaching Canada, but these often retain significant strength and the potential to cause heavy rainfall and strong winds. T. Chen and Di Luca (2025) showed that, in the portion of northeastern North America that includes parts of Manitoba, Ontario, Quebec, and New Brunswick, more than 70% of hourly extreme wind speeds are associated with mid-latitude cyclones (including both extratropical storms and North Atlantic hurricanes). In the Atlantic provinces, this proportion exceeds 80% (T. Chen and Di Luca, 2025). Malinina et al. (2025) found that, in Nova Scotia, New Brunswick, Prince Edward Island, and Newfoundland, North Atlantic hurricanes contribute to about 25% of daily maximum wind speed maxima during the hurricane season, defined as the period from June to October (Figure 8.21).

Figure take-away: Extreme winds are often associated with extratropical cyclones and North Atlantic hurricanes in Canada, particularly in central and eastern Canada.

Figure title: Seasonal association between extreme winds and extratropical cyclones and North Atlantic hurricanes

Figure 8.20: Maps of Canada showing the proportion (as a %) of extreme hourly 10-m wind speeds (> 99th percentile) associated with extratropical cyclones and North Atlantic hurricanes in a) June, July, and August (JJA); b) December, January, and February (DJF); c) September, October, and November (SON); and d) March, April, and May (MAM) during the 2001 to 2020 period. Hourly wind speeds were obtained from the ERA5 reanalysis dataset. The number shown in the top right-hand corner of each map represents the averages for Canada. Black hatching indicates regions where cyclones cannot be identified by the algorithm due to topography exceeding 1000 m in elevation. Adapted from: T. Chen and Di Luca (2025); data source (hourly wind speeds): Hersbach et al. (2020).

8.4.3.1: Past changes and attribution

Chapter 4 concluded that there was low confidence in the observed changes in extratropical cyclones (section 4.4) and North Atlantic hurricanes affecting Canada (section 4.6), due to the strong variability in these storms over years or decades and the high sensitivity of trends to the reanalysis data chosen and the specific detection method used. To date, the only study on the attribution of changes in extreme winds associated with North Atlantic hurricanes impacting Canada is an event attribution study focusing on 2022 Hurricane Fiona (Malinina et al., 2025) (Box 8.5). The study analyzed daily maximum wind speeds during the hurricane season (June-October) in Atlantic Canada (Figure 8.21). Fiona was assessed to be a roughly 1-in-100-year event, based on the daily maximum winds. Owing to human-caused climate change, winds at least as strong as those observed in Atlantic Canada during Fiona have become 5.3 times (range: 2.8 to 11.1 times) more likely today than during the 30-year reference period (1950 to 1979).

Figure take-away: Hurricane landfalls cause extreme winds in Atlantic Canada.

Figure title: Extreme wind speeds from hurricane landfalls

Figure 8.21: Hourly wind speed maxima (in m/s) during the hurricane season (June-October) in Atlantic Canada during the 1940 to 2023 period based on a time series of ERA5 reanalysis data. The red dots show the seasonal maxima associated with landfalls of North Atlantic hurricanes. Because the ERA5 reanalysis dataset is a gridded product, the wind speeds recorded at the individual stations during specific hurricane landfalls can differ from the values presented in the figure. Adapted from: Figure 3 in Malinina et al. (2025).

8.4.3.2: Future changes

The projected changes in the frequency and intensity of extratropical storms affecting Canada (including the poleward shift in storm tracks) are assessed in Chapter 4, section 4.4, as having low confidence, due to high model uncertainty and a lack of theoretical understanding. The projected changes in extreme winds associated with extratropical storms in Canada are assessed with low confidence, owing to uncertainties in how cyclones and extreme winds are identified, the large natural variability of these events, and limitations in their representation in climate models (Chang, 2018; Dolores-Tesillos et al., 2022; Gentile et al., 2023; Seiler and Zwiers, 2016b). Specifically, there is some evidence for, but overall low confidence in, a projected decrease in the frequency of cyclones with extreme near-surface winds in the northwest Atlantic during the extended winter season (Chang, 2018; Dolores-Tesillos et al., 2022; Kumar et al., 2015; Seiler and Zwiers, 2016b; Zappa et al., 2013).

In addition, the projected changes in the frequency of North Atlantic hurricanes affecting Canada were assessed in Chapter 4, section 4.6, as uncertain, with low confidence in changes in the frequency of North Atlantic hurricanes and associated changes in peak wind speeds, particularly those affecting Canada. Projected changes in the total number of extratropical transitions of hurricanes were also assessed as uncertain, as most of the available evidence comes from single-model studies, which disagree on the direction of the projected changes. There is some agreement that transitioning cyclones may become more destructive, as measured by integrated kinetic energy (Cheung and Chu, 2023) or surface winds (Jung and Lackmann, 2023). Nevertheless, large model uncertainties and high natural variability result in low confidence in the projections of North Atlantic hurricanes and their associated wind extremes.

8.4.4: Thunderstorm-related winds

Tornadoes and downbursts (powerful bursts of wind that descend to the ground) are the main damaging wind-related phenomena associated with thunderstorms. Most tornadoes occur in southern Canada between May and September. However, tornadoes have been recorded in all provinces and territories, and in every month except February. Extreme, when applied to tornadoes, usually means having a rare level of intensity—one typically capable of causing catastrophic damage. Such tornadoes are often referred to as violent tornadoes, defined as having a rank of 4 or 5 on the Fujita (F) or Enhanced Fujita (EF) scales (where 5 is the highest; note that the EF Scale replaced the original F scale in Canada on April 1, 2013).

With downbursts, there are no recorded instances in Canada of violent downbursts (those intense enough to cause catastrophic damage rated at 4 or above on the F or EF scale). The most intense downbursts have caused damage rated at 2 on the F or EF scale. Extreme, when applied to downbursts, typically refers to the extent of the damage on the ground caused by the downburst. A family of downburst clusters that generates a semi-continuous swath of damage that is 100 km or wider and 650 km or longer is known as a derecho. Such an extreme windstorm affected southern Ontario and Quebec in May 2022, resulting in 16 fatalities, 32 injuries and over Can$1 billion in insured losses. Derechos occur in Canada roughly as often as violent tornadoes. Although violent tornadoes and derechos are extremely rare events in Canada as a whole, reliable estimates of their frequency are still in the process of being obtained.

8.4.4.1: Past changes

Around the globe, the extent to which tornadoes and downbursts are systematically documented varies widely. Therefore, analysis of a set of high-quality global observations is not yet possible. In Canada, the focus of research over the last two decades has been on increasing the quality and length of the tornado record to obtain a better understanding of Canada’s tornado climatology in order to study long-term trends (V. Y. S. Cheng et al., 2013; Sills et al., 2020). The systematic documentation of downbursts began only recently (Sills et al., 2020).

One observational study (Sills et al., 2022) looked at the historical trend (1875−2019) for significant tornadoes (F/EF2 and greater) in southern Ontario, the region in Canada with the longest and highest-quality data record. It found that, while the number of significant tornadoes does not appear to be changing in that region, there is a statistically significant trend of such tornadoes occurring later in the season (that is, late summer to early fall).

Overall, the robust identification of historical trends in thunderstorm-related extreme winds is difficult due to limited evidence and the lack of high-quality data. As more data become available, additional research will be needed to better understand the degree to which climate change is influencing these trends.

8.4.4.2: Future changes

Tornadoes and downbursts occur over extremely short timescales (hourly to sub-hourly) and have a small footprint (less than a kilometre to tens of kilometres); they are not explicitly represented in current climate models used for projecting future climate change. Analyses of climate change simulations specifically involving tornadoes and downbursts in Canada have yet to be published in the peer-reviewed literature. However, as was mentioned in the section on hail (section 8.3.5), one study has attempted to assess the effects of climate change on severe thunderstorm events by using 45 km CRCM simulations to examine future changes in convective instability and vertical wind shear (Paquin et al., 2014). While the authors found an overall increase in the conditions conducive to severe thunderstorms with at least 25 mm/day of convective precipitation, there were no explicit results for tornadoes or downbursts.

8.4.5: Confidence terms in key messages: summary of evidence

Key Message 8.10: There is insufficient evidence to assess historical changes in extreme winds in Canada due to limited station coverage and representativeness, significant uncertainties in reanalysis products, and the paucity of studies examining trends in wind extremes. There is very low confidence in the direction and magnitude of future changes in extreme winds due to the limited number of studies and the substantial uncertainty related to model projections of wind-related phenomena.

Extreme winds result from intense pressure gradients generated by various types of storms, including extratropical cyclones, tropical cyclones, North Atlantic hurricanes, isolated thunderstorms, and organized convective systems. Certain factors associated with the development of storms—such as the increase in atmospheric moisture and latent heat release caused by rising temperatures—are well understood in the context of climate change. However, other factors, particularly those linked to large-scale atmospheric circulation (Chapter 4) and fine-scale processes, remain highly uncertain.

Detecting historical trends in extreme wind speeds, as well as in the frequency and intensity of various types of storms, often relies on the use of reanalysis data, due to the scarcity of high-quality, consistent ground-level and upper-air observations in Canada. While reanalysis datasets have the advantage of providing quasi-continuous spatial and temporal coverage, such continuous coverage is lacking in the observations used to create these datasets, which can lead to artificial trends. In North America, studies have demonstrated that trends in extreme wind speeds are highly sensitive to the reanalysis product selected and the period analyzed, further complicating the assessment of important trends. In addition, the relatively coarse horizontal resolution of reanalysis data does not allow some types of storms, including thunderstorms and thunderstorm systems, to be detected. Although specific data on tornadoes and thunderstorms are now being collected in Canada, it may be a number of years before a clear national picture emerges. Therefore, there is insufficient evidence to assess historical changes in wind extremes in Canada.

The assessment of very low confidence in projected changes in wind speeds stems from several key challenges. First, the signal-to-noise ratio is low, as the projected changes are small compared with the wide variability in wind speeds from year to year. Second, wind speeds have received considerably less research attention than temperature and precipitation, resulting in a limited number of available studies. Third, most current global and regional climate models lack the spatial resolution and the adequate representation of physical processes—such as convection, and boundary layer and wildfire-atmosphere feedbacks—that are needed to capture the physical mechanisms driving extreme wind. This adds further uncertainty to projections, particularly in the case of localized phenomena such as tornadoes and downbursts. Developing reliable methods to assess future changes in these events remains a major challenge in climate modelling globally and in Canada.

8.5: Water cycle extremes

Key Message 5.8: Meteorological and agricultural droughts are projected to be longer and more frequent and intense across central and southern Canada during summer, and to be more prominent with higher amounts of global warming and at the end of the century (high confidence). Summer hydrological droughts are also projected to be longer and more frequent and intense in many regions of southern Canada, mainly because of increased evaporation and lower runoff from mountainous regions (low confidence). Historically, periodic droughts have occurred across much of Canada, but no long-term changes in their frequency are detectable (high confidence).

Key Message 5.9: Over the period of observation, there have been no consistent trends in streamflow flood events across the country (high confidence). Streamflow-related floods in Canada are driven by multiple factors, including extreme precipitation, rapid snowmelt, rain-on-snow events, and ice jams, with complex interactions among these drivers.

Key Message 5.10: Projected increases in extreme precipitation are expected to lead to more frequent and intense flash flooding across Canada (high confidence). Warmer temperatures are expected to lead to the earlier occurrence of snowmelt, rain-on-snow events, and ice jam breakups, resulting in earlier streamflow-related spring floods (medium confidence). However, their future frequency remains uncertain because of the interactions among rising temperatures, reduced snow cover, and the complex dynamics of ice jam–related and snowmelt-related floods.

8.6: Oceans

Key Message 7.3: More frequent and intense marine heatwaves have been observed in the Pacific and Atlantic oceans around Canada since the 1980s and are expected to continue to increase in all oceans, including the Arctic, because of human-caused climate change (high confidence).

Key Message 7.8 Mean and extreme wave heights have increased in Arctic and sub-Arctic regions, primarily as a result of the warming-driven reduction in sea ice (high confidence). Wave heights in the northwest Atlantic have also increased over the last few decades (medium confidence), but the changes in ice-free areas that would result in increased wave heights cannot be directly attributed to human-caused climate change. Wave height trends in the northeast Pacific are not statistically significant. Extreme storm surges have increased in areas of the western Arctic, Hudson Bay, and Atlantic Canada (low confidence).).

Key Message 7.9 The projected lengthening of the Arctic ice-free season will cause increases in mean and extreme wave heights (high confidence) and storm surges (medium confidence). In areas of Atlantic Canada not affected by sea ice, mean wave heights are projected to decrease (medium confidence), while weak changes are projected in the northeast Pacific (low confidence). Projections of extreme wave heights in areas without sea ice provide limited evidence of increases in Atlantic Canada (low confidence).

Key Message 7.10: Extreme sea-level events have increased in frequency and magnitude in places along Canada’s coastline where relative sea level has risen over the past century, such as southern Atlantic Canada, the western Arctic and British Columbia (high confidence).

Key Message 7.11: Extreme sea-level events are projected to occur more often and to become larger because of future increases of relative sea-level rise in many parts of Canada (high confidence). The projected decline of sea ice along Canada’s Arctic and Atlantic coasts will result in increased waves and storm surges, which will exacerbate extreme sea-level events (high confidence).

Key Message 7.13: Beneath the surface, oxygen has declined over many decades in the offshore and coastal waters of the northeast Pacific, in the Estuary and Gulf of St. Lawrence, and on the Scotian Shelf (high confidence), but the processes responsible vary by region and are not fully understood. Since biological respiration both consumes oxygen and adds carbon dioxide, acidification is exacerbated in regions experiencing oxygen declines (high confidence). Year-to-year variations in water circulation and biological activity have caused extreme low oxygen and high acidity events in the subsurface ocean surrounding Canada (medium confidence).

Key Message 7.14: Over the rest of the 21st century, the oceans surrounding Canada are projected to continue to absorb anthropogenic carbon dioxide, to increase in acidity (very high confidence), and to lose oxygen in the subsurface (medium confidence). The level of projected acidification in the latter half of this century depends strongly on the level of future carbon dioxide emissions (very high confidence).

8.7: Compound events

Compound weather and climate events, referred to from now on simply as compound events, result from multiple meteorological or climatological conditions that act in concert to contribute to societal or environmental risks (Zscheischler et al., 2018). When compound events involve multiple conditions occurring together (such as strong wind and heavy rain, or hot temperatures and dry, windy conditions), they are referred to as multivariate events (Zscheischler et al., 2020). When they occur in succession (such as successive rainfall events leading to flooding), or simultaneously in multiple regions (such as droughts across many food-producing regions, or dry conditions in one area but wet conditions nearby) (Brimelow et al., 2014), they are categorized as temporally compounding events or spatially compounding events, respectively. Preconditioned events take place when a single driver occurs in combination with existing conditions that can enhance impacts (such as rain-on-snow flooding events) (Chapter 5, section 5.7). Note, however, that the categories are not mutually exclusive. For example, a compound coastal flooding event can be both a preconditioned event (prolonged rainfall leads to soil saturation, increasing the risk of rapid flash flooding) and a multivariate event (simultaneous heavy rainfall, high river levels, and storm surge contribute to flooding).

An important characteristic of compound events is their ability to have impacts that are often greater than the sum of the impacts of the individual drivers. In fact, some of the contributing conditions might not be extreme in themselves but, when acting together, can lead to a hazard. This means that traditional methods that only consider a single driver or variable at a time cannot be used to analyze compound events; the complex connections and interactions between multiple drivers must be considered together. From a meteorological perspective, certain weather phenomena may be more likely to trigger a compound event. For example, atmospheric rivers are associated with both rainfall and wind (Chapter 4, Figure 4.11), which could result in a compound wind and rainfall event (section 8.7.3). An atmospheric river could also lead to compound flooding through a combination of warming-driven snowmelt and rain-on-snow (Chapter 5, section 5.7).

The Summary for Policymakers (SPM) in the IPCC AR6 WGI report notes that, globally, “human influence has likely increased the chance of compound extreme events since the 1950s. This includes increases in [the] frequency of concurrent heatwaves and droughts on the global scale (high confidence), fire weather in some regions of all inhabited continents (medium confidence); and compound flooding in some locations (medium confidence)” (IPCC AR6 WGI SPM A.3.5) (IPCC, 2021). In the future, climate change may lead to more intense and more frequent compound events by making certain drivers more likely or affecting the way they interact (Zscheischler et al., 2018). Examples of multivariate compound events in Canada include the compound coastal flooding events in eastern Canada caused by Hurricane Juan (2003), Hurricane Dorian (2019), and Hurricane Teddy (2020) (Jalili Pirani and Najafi, 2022) (see Figure 8.21 for an illustration of how hurricanes can also contribute to wind extremes). Several parts of Canada have been identified as global hotspots where multiple different kinds of wet—and potentially flood causing—conditions occur together (section 8.7.2). Canada also has hotspots where dry and hot conditions occur together, such as meteorological or hydrological drought combined with extreme hot temperatures, which can lead to increased fire danger (Ridder et al., 2020) (section 8.7.1).

Because compound events can arise through many different pathways, this chapter cannot assess every possible combination. The assessment below concentrates on a subset of multivariate compound extremes, specifically those where two or more drivers occur together. Other categories remain highly relevant, however. Case Story 8.1, “Smoke and Peaches,” gives an example of a temporally compounding event where the impacts on society of fruit crop losses from a mid-winter warm spell and subsequent freeze were later compounded by pervasive wildfire smoke from an active fire season. Although none of the events were unprecedented on their own, their sequence profoundly disrupted Métis food-harvesting and preservation practices, underscoring how moderate events can interact over time and lead to devastating economic and cultural impacts. Likewise, several phenomena discussed in Chapter 5, such as rain-on-snow flooding, spring freshet floods, and agricultural or hydrological droughts (Chapter 5, sections 5.6 and 5.7), can also be viewed through a compound-event lens, even though they are not reassessed here.

With this broader context in mind, the following subsections assess observed and projected changes in four multivariate compound extremes: fire weather and wildfires (concurrent hot, dry, and windy conditions) in section 8.7.1; compound coastal flooding (precipitation, river flooding, storm surge, and waves acting together) in section 8.7.2; compound wind and rainfall (simultaneous occurrence of heavy wind and rainfall) in section 8.7.3; and human-perceived heat stress (hot temperatures combined with high humidity) in section 8.7.4.

8.7.1: Fire weather and wildfires

Key Message 8.11: The fire season has lengthened in most parts of Canada (high confidence) and is projected to continue to lengthen as the global average temperature increases (high confidence). Fire weather—characterized by hot, dry, and windy conditions conducive to wildfires—has increased in Alberta and British Columbia (medium confidence), with some indications of increases in other regions (very low confidence). The frequency and intensity of extreme fire weather conditions are projected to increase in most regions of Canada as global average temperature rises (high confidence).

In Canada, wildland fires (wildfires)—which originate from both human and natural causes—are a frequent occurrence, with an average of roughly 7,500 fires (based on the 1959 to 2015 period) (Hanes et al., 2019), burning 2.1 million ha annually (based on the 1986 to 2022 period) (Jain, Barber, et al., 2024; Skakun et al., 2024). Wildfires result from a complex interplay of factors, and require available burnable biomass (the fuel), a source of ignition, and conducive weather conditions. Typically, the potential for fire ignition and spread increases under surface weather conditions that are hot, dry, and windy. Increased convection due to atmospheric instability, as well as fire-atmosphere feedbacks, can also influence fire behaviour. This section focuses on changes in fire weather (compound hot, dry, and windy conditions) and in the length of the fire season (the period of the year when wildfires occur). Changes in area burned and wildfire emissions are assessed in Chapter 9, section 9.5.1.7.

Various systems for rating fire danger have been developed to capture these fire-weather relationships. The Canadian Forest Fire Weather Index (FWI) System (Figure 8.22) (Van Wagner, 1987), part of the Canadian Forest Fire Danger Rating System (CFFDRS) (Stocks et al., 1989), is used extensively by local governments, fire managers, and researchers in Canada and internationally. Because most fire weather metrics are a function of several meteorological variables, extreme fire weather events can themselves be considered compound events. For example, the FWI System uses daily surface temperature, relative humidity, wind speed, and precipitation as inputs (Van Wagner, 1987), and its outputs include three potential fire behaviour indices: the Initial Spread Index (ISI); the Build-up Index (BUI) and an overall rating of fire danger, the Fire Weather Index (FWI) (Figure 8.22). Extreme ISI values may be related to extreme surface winds, while extreme BUI values may correspond to extreme drought conditions. Thus, the FWI, which depends on both components, may have extreme values when either or both the ISI or BUI are extreme. Past and future changes in extreme values of the FWI System indices are influenced by the combined changes in the underlying meteorological variables. Nevertheless, a warming climate is expected to lead to increases in the frequency and intensity of fire weather extremes in many regions (Quilcaille et al., 2023; Seneviratne et al., 2021; Van Vliet et al., 2024). For example, although precipitation and temperature are both projected to increase, when projecting fuel moisture, precipitation increases are not in general enough to compensate for increases in temperature (Flannigan et al., 2016) in most regions (Van Vliet et al., 2024).

In Canada, the largest 3% of fires are responsible for 97% of the total area burned, with these fires typically occurring under more severe fire weather conditions. In general, fires become large when a few days of extreme fire weather enable the large linear spread of the fire, which may challenge fire suppression efforts. This concept has been quantified by defining so-called potential spread days, when fire weather metrics (such as the FWI) exceed a regionally dependent threshold (X. Wang et al., 2014). Although there is no consistent definition of extreme fire weather, it is often defined by values above a specified threshold or percentile of a given fire weather metric (for example, Hanes et al., 2021). For example, an FWI value may be considered extreme if it is greater than the 95th or 99th percentile (that is, within the top 5% or 1%, respectively, of all values recorded for that region) (Dowdy et al., 2010; Jain et al., 2022). Increases in fire weather extremes are associated with a higher probability of short-interval reburns (that is, less than 20 years between fires) in Canada (Whitman et al., 2024) and an increased frequency of overnight burning conditions (Luo et al., 2024).

Figure take-away: The Canadian Forest Fire Weather Index System describes wildfires’ potential to ignite and spread and their potential intensity based on weather conditions in a standard pine forest.

Figure title: Fire Weather Index System

Figure 8.22: Flow chart representing the Fire Weather Index (FWI) System. Different weather variables are used to calculate the different component indices. The FWI System includes three moisture codes that represent fuel dryness, including the dryness of litter fuels (Fine Fuels Moisture Code, or FFMC) and the upper duff layer of decaying organics (Duff Moisture Code, or DMC), as well as the longer-term drying of denser, deeper duff layers (Drought Code, or DC). This allows two indices associated with fire behaviour to be calculated (Initial Spread Index, or ISI, for fire spread; Build-up Index, or BUI, for fire consumption), as well as an overall estimate of potential fire intensity known as the Fire Weather Index (FWI). More details about the interpretation of the different components can be found in Wotton (2009). Adapted from: Van Wagner (1987).

Fire weather has increased around the world, including some parts of North America (Jain et al., 2022; M. W. Jones et al., 2022; Z. Liu et al., 2022). Globally, the intensity of extreme fire weather increased by 12 to 14% from 1979 to 2020, based on the 95th percentile of FWI, ISI, and Vapour Pressure Deficit (a measure of atmospheric humidity) values, and the intensity of extreme fire weather increased in one quarter to one half of the burnable area across the globe. In North America, including regions of western Canada, 15 to 38% of trends observed in the intensity of extreme fire weather were significant (Jain et al., 2022). Regional studies for Alberta (Whitman et al., 2022) and British Columbia (Parisien et al., 2023) found increases in fire weather or related metrics that drove growth in the area burned. In other parts of Canada, some increases in fire weather were observed, although the changes were generally not significant and year-to-year variability was high (Jain et al., 2022).

Lower humidity and higher temperatures are the main drivers of observed increases in extreme fire weather (Jain et al., 2022), consistent with the drying of fuels observed in many productive ecosystems (Ellis et al., 2022). Extreme fire weather events are also associated with large-scale atmospheric patterns, including persistent atmospheric blocking (stationary weather systems that bring persistent high pressure, warm surface temperatures, and cloudless skies, and often lead to heatwaves) (Chapter 4, section 4.4) (Jain and Flannigan, 2021; Sharma et al., 2022). Changes in these atmospheric patterns could, therefore, also lead to changes in extreme fire weather; however, there is currently low confidence in any projections of changes in persistent blocking or similar large-scale atmospheric circulation patterns (Chapter 4).

The fire season refers to the period each year when wildfires are most likely to occur. In Canada, the length of the fire season has increased, expanding by about two weeks between 1959 and 2015 (Hanes et al., 2019). This finding is consistent regardless of whether fire season length is calculated using fire weather proxies based on maximum daily temperature thresholds as defined by Wotton and Flannigan (1993) or fire occurrence data (Hanes et al., 2019; Jain et al., 2017; M. W. Jones et al., 2022) and is also consistent with global findings (Jolly et al., 2015). Longer fire seasons are associated with decreasing snow cover durations in many parts of the country (see Chapter 6, section 6.2), and result in more days with the potential for extreme fire weather and for wildfires to ignite and spread.

Since CCCR2019 was published, additional major Canadian wildfire seasons have been assessed for event attribution purposes (that is, determining the influence of human-caused climate change on the likelihood or intensity of the event; Box 8.1). For example, for the extreme 2017 wildfire season in British Columbia, human-caused climate change made observed fire weather metrics two to four times more likely and increased the area burned by a factor of between 7 and 11 (Kirchmeier-Young, Gillett, et al., 2019). The 2017 wildfire season was also notable because it was the first time during the satellite observation era when wildfire smoke and associated aerosol particles reached high into the stratosphere (up to 23 km above the Earth’s surface), transported by wildfire-induced clouds. As a result, the 2017 wildfires caused a volcano-like effect by decreasing the amount of incoming solar radiation to the surface (Bourassa et al., 2019; Kloss et al., 2019; Malinina et al., 2021; Peterson et al., 2018; P. Yu et al., 2023). In 2023, Canada again experienced a record-setting wildfire season, this time at the national scale. Analyses have shown that human-caused climate change increased the likelihood of the area burned, the length of the fire season, and extreme fire weather that occurred across most of the country that year being as great as they were (Box 8.4) (Barnes et al., 2025; Kirchmeier-Young et al., 2024). Changes in fire weather extremes have also been attributed to human-caused climate change at the global scale (Abatzoglou et al., 2019; Z. Liu et al., 2022; Touma et al., 2021).

In Canada, about half of wildfires are ignited by lightning (the other half are started by human activities), but lightning-started fires have accounted for more than 90% of the area burned historically (Hanes et al., 2019). The IPCC AR6 WGI report noted limited evidence for observed trends in lightning due to a lack of long-term observations (Seneviratne et al., 2021). However, Canadian lightning observations show some evidence of a decrease in lightning in central and eastern Canada and the country as a whole, but an increase in western and northern Canada (Burrows et al., 2025; Kochtubajda and Burrows, 2020). We note that the changes in the area burned in Canada (Chapter 9, section 9.5.1.7) are well explained by changes in temperature and fire weather (Gillett et al., 2004; Kirchmeier-Young et al., 2024). Climate model projections under future warming scenarios suggest a small increase in lightning at northern mid-latitudes to high latitudes, although the changes are uncertain and vary by location (Finney et al., 2018; Janssen et al., 2023; Whaley et al., 2024). More frequent lightning would mean more opportunities for wildfires to ignite.

CCCR2019 found an increase in fire weather conditions in Canada under additional increases in global average temperatures (X. Zhang et al., 2019), which is also supported by more recent literature. Fire weather metrics are projected to increase globally under most climate change scenarios (Abatzoglou et al., 2019; Quilcaille et al., 2023), as well as in Canada (Van Vliet et al., 2024) (Figure 8.23). A study focusing on Alberta and British Columbia projected more potential spread days (days with high FWI values indicating suitable weather conditions for fires to spread, even under suppression efforts) under the high emissions scenario (RCP8.5) (Jain et al., 2020). Projections of fire weather are more uncertain in northwestern Canada, where climate models show less agreement (Quilcaille et al., 2023), due to the greater uncertainty surrounding the magnitude of increases in precipitation and how they will counteract the effects of warming temperatures (Van Vliet et al., 2024). Fire seasons in Canada are also projected to continue to lengthen with further warming (Figure 8.23). The average fire season length in Canada is projected to increase by 15, 21, and 37 days for the 2071 to 2100 period relative to the 1971−2000 period under the low (RCP2.6), intermediate (RCP4.5), and very high (RCP8.5) emissions scenarios, respectively (Van Vliet et al., 2024). Overall, continued global warming is expected to result in more of the compound hot, dry, and windy conditions that are conducive to wildfires and a longer period each year when these conditions occur.

Figure take-away: The fire season is projected to become longer across Canada, and extreme fire weather is projected to increase in most regions.

Figure title: Projections of fire weather for Canada

Figure 8.23 Maps showing projected changes in two fire weather metrics in Canada: a) the Extreme Build-up Index (BUI), represented by the 95th percentile for the May–September fire season, and b) the fire season length. The Build-up Index is a unitless metric describing the potential fuel available for consumption, larger values of which indicate the increased potential for wildfire spread. Projections are based on the ensemble median from the CanLEAD-FWI (fire weather index) dataset (Van Vliet et al., 2024) at three different levels of global warming (1.5°C, 2°C, and 4°C above the pre-industrial level), with changes expressed in relation to the recent past (that is, 1°C of global warming). See Chapter 3, Box 3.1, for an explanation of how global warming levels translate to increases in Canadian average temperatures over different time periods. Adapted from: Van Vliet et al. (2024).

8.7.2: Compound coastal flooding

Key Message 8.12: Compound coastal flooding arises from jointly occurring climate conditions, such as heavy rain during episodes of extreme water levels. Such co-occurring drivers have increased in frequency and intensity in some parts of Canada, with evidence primarily from Atlantic locations (low confidence), and are projected to become more frequent and intense at locations along the Atlantic, Pacific and Western Arctic coastlines (medium confidence). These projected increases are mostly due to rising sea levels and increases in the frequency and intensity of extreme precipitation events (medium confidence).

Compound coastal flooding occurs when multiple factors occurring together, such as extreme streamflow, heavy rain, and high water levels, lead to flooding along ocean coastlines or the shoreline of large lakes (for example, the Great Lakes). High coastal water levels can result from a combination of several drivers, including storm surge, high tides, and extreme waves (Chapter 7, section 7.6). This phenomenon can also be exacerbated by processes that take place sequentially, such as extreme coastal water levels combined with heavy precipitation falling on an already wet soil due to a previous period of prolonged rain. Since the publication of CCCR2019, a few studies on compound coastal flooding have been carried out for Canada (Jalili Pirani and Najafi, 2022, 2023a, 2023b). While these studies do not explicitly account for the occurrence or impacts of flooding, they improve our understanding of this compound hazard through multivariate assessments of co-occurring coastal, pluvial, and fluvial drivers that are potentially responsible for coastal compound flooding.

On the Pacific and Atlantic coasts and the shores of the Great Lakes, where sufficiently long tide gauge records are available, the co-occurrence of drivers responsible for flooding, in particular a combination of extreme precipitation and elevated water levels, was identified at approximately 80% of the studied locations (Jalili Pirani and Najafi, 2023a). Ocean waves (which are generally not well captured by tide gauges due to their sheltered locations) can also significantly contribute to extreme water levels (Chapter 7, section 7.6) and to compound coastal flooding, given the documented statistical dependence between extreme storm surges and wind waves (Marcos et al., 2019).

Moreover, the likelihood of compound flooding is increasing in several regions of Canada as a result of relative sea-level rise (Chapter 7, section 7.6) and other relevant drivers. Historical observations of precipitation, streamflow, and water levels (1960−2015) suggest increased risks of both individual events (caused by a single driver) and compound flooding drivers along the Atlantic coast, where streamflow, total water levels, and storm surge generally show increasing trends. In contrast, the Pacific coast and the shores of the Great Lakes exhibit more variable patterns, with a mix of increasing, decreasing, and nonsignificant trends depending on location and driver, leading to less consistent evidence of change in these regions (Jalili Pirani and Najafi, 2020). These results are in line with the general upward trend in annual precipitation (Chapter 2, section 2.5) and extreme precipitation (section 8.3.1) in Canada, and the increasing magnitude and frequency of extreme sea levels in southern Atlantic Canada and British Columbia (Chapter 7, section 7.6).

In multiple Canadian regions, coastal water levels are positively correlated with precipitation and, to a lesser extent, streamflow, making them more likely to become extreme simultaneously (Jalili Pirani and Najafi, 2022). The likelihood of compound coastal flooding in Canada was estimated to increase by up to 50% if these interrelationships are accounted for, compared to estimates using a traditional approach that analyzes one variable at a time (Jalili Pirani and Najafi, 2022, 2023a, 2023b).

According to a global-scale study, the likelihood of concurrent extreme precipitation and storm surge events is highest at mid-latitudes in fall and winter, coinciding with the season of maximum extratropical cyclone activity (Bevacqua et al., 2020). Again, high ocean waves are likely to co-occur with these extreme conditions, and they are primarily driven by cyclone activity (Chapter 7, section 7.5). A recent example of coastal flooding in Canada due to the combined action of extreme storm surges and waves is the flooding that occurred during Hurricane Fiona (September 2022), which also led to significant erosion and damage in Atlantic Canada (Box 8.5). In locations such as Port aux Basques, Newfoundland and Labrador, this occurred near high astronomic tide, which further aggravated coastal damage. In other locations, such as Prince Edward Island, the flooding could have been even worse if the incoming surges from the Atlantic Ocean and Gulf of St. Lawrence, respectively, had occurred simultaneously (Mulligan et al., 2023). Even so, significant erosion took place on parts of the island (Cantelon et al., 2024).

The literature on projected changes in compound coastal flooding events in Canada is more limited, but the available studies point to future increases, particularly in southern Atlantic Canada, the western Arctic, and British Columbia, where the relative sea level is rising (Chapter 7, section 7.6). This agrees with the assessment in Chapter 11 of the IPCC AR6 WGI report (Seneviratne et al., 2021) that there is high confidence that the occurrence and magnitude of compound flooding in coastal regions will increase in the future due to both sea-level rise and increases in heavy precipitation. Indeed, the projected increase in precipitation has been found to be a key driver of the projected increase in the co-occurrence of extreme precipitation and storm surge along the west and east coasts of Canada. Additionally, changes in the dependence between these two drivers are a contributing factor in some coastal locations (Bevacqua et al., 2020).

The lack of integrated projections of flooding drivers in the Canadian Arctic has resulted in uncertainty about future changes in compound coastal flooding in this region. However, in the western Arctic, where flood drivers are projected to increase, coastal compound flooding is likely to increase as well. These flood drivers include the projected increase in sea-level rise and extreme sea levels (Chapter 7, sections 7.4 to 7.6), Arctic precipitation (for example, McCrystall et al., 2021) (Chapter 3, section 3.5), increased wave activity (for example, Casas‐Prat and Wang, 2020) (Chapter 7, section 7.5), and larger storm surges associated with sea ice decline (for example, Kim et al., 2021) (Chapter 7, section 7.5).

Moreover, these compound flood drivers will also interact with other coastal processes, such as coastal erosion, as pointed out by recent studies in similar Arctic conditions. For example, a recent study in Alaska, which considered relative sea-level rise in combination with coastal erosion, showed a drastic increase in the area of coastline impacted by these compound effects (Creel et al., 2024).

8.7.3: Compound wind and rainfall

Key Message 8.13: There is insufficient evidence to assess historical changes in compound wind and rainfall events in Canada, due to limited studies focusing on observed trends. Compound extreme wind and rainfall events are projected to increase (medium confidence), driven mainly by the greater frequency of future extreme rainfall, but with very low confidence in the regional pattern and magnitude of changes.

Simultaneously occurring wind and rain extremes pose an important compound weather hazard (Yaddanapudi et al., 2022; Zhu et al., 2024). Strong winds coinciding with heavy rainfall are considered in the design and construction of buildings in Canada; wind pressures combined with rainwater can cause an inflow of water through joints or cracks in building envelopes, which can lead to severe damage. Specifically, driving-rain wind pressure is defined in the National Building Code of Canada as the hourly wind pressure that occurs when rainfall exceeds 1.8 mm/h (National Research Council of Canada, 2015), and is an important wind load that must be incorporated into design standards.

As shown in Figure 8.24a, the probability of wind and precipitation extremes occurring simultaneously is very high along Canada’s Pacific and Atlantic coasts (about 45%) and lower in inland Canada (between 5% and 20%), with an average of 16.2% in Canada as a whole. In Canada and other mid-latitude regions, compound wind and precipitation extremes are often caused by atmospheric rivers (Chapter 4, section 4.5), extratropical cyclones (Chapter 4, section 4.4; section 8.4.3), and North Atlantic hurricanes. In Canada, upwards of 70% of co-occurring wind and precipitation extremes are associated with atmospheric rivers, particularly along the Pacific and Atlantic coasts (Chapter 4, Figure 4.11d). Figure 8.24b shows that, in central and eastern Canada, roughly 80% or more of compound wind and precipitation extremes are associated with extratropical cyclones or North Atlantic hurricanes. Note, however, that atmospheric rivers and extratropical cyclones are not independent phenomena and often occur simultaneously (Guo et al., 2020). For example, along the west coast of Canada, around 80% of atmospheric rivers are associated with an extratropical cyclone, although only 45% of extratropical cyclones have a paired atmospheric river (Z. Zhang et al., 2019).

Figure take-away: Compound wind and precipitation extremes frequently occur in association with cyclones in North America.

Figure title: Compound occurrence of wind and precipitation extremes and their association with cyclones

Figure 8.24: Maps illustrating the occurrence of compound wind and precipitation extremes and their association with extratropical cyclones and North Atlantic hurricanes. a) Map showing the frequency (expressed as a percentage) of the simultaneous (compound) occurrence of hourly wind and precipitation extremes in Canada, with the average for the country as a whole shown in the bottom right-hand corner. b) Map showing the ratio (expressed as a percentage) of compound wind and precipitation extremes occurring in association with extratropical cyclones and North Atlantic hurricanes to all compound wind and precipitation extremes, with the average for Canada shown in the bottom right-hand corner. Precipitation and wind extremes were calculated using the 99th percentile of hourly values between January 2001 and December 2020 obtained from the ERA5 reanalysis. Adapted from: T. Chen and Di Luca (2025); data source: Hersbach et al. (2020).

The magnitude of compound wind and precipitation extremes is projected to increase globally (Ridder et al., 2022), including in most coastal regions of the North Atlantic (Yaddanapudi et al., 2022) and most land areas, including those in Canada (Zhu et al., 2024). Consistent with global studies, regional climate models project that driving-rain wind pressure and wind-driven rain (the horizontal component of rainfall influenced by wind) will increase in Canada, with the area affected and the relative magnitude of the increases tending to increase linearly with increasing levels of global warming (Figure 8.25b) (Jeong and Cannon, 2020; Jeong et al., 2020). These increases are primarily caused by increased extreme rainfall, as well as the increase in the fraction of precipitation falling as rain rather than snow. Overall, confidence in the spatial pattern and magnitude of the projected changes is very low, because of the substantial uncertainty in projected wind and rainfall extremes, including contributions from changes in atmospheric circulation.

Figure take-away: Driving-rain wind pressure is projected to increase in Canada.

Figure title: Historical and projected changes in five-year return period driving-rain wind pressures

Figure 8.25: Maps showing historical five-year return period driving-rain wind pressure in pascals (Pa) and projected changes in it (as a percentage) at different global warming levels. a) Historical five-year return period driving-rain wind pressure during the 1986 to 2016 period obtained from a CanRCM4 ensemble simulation. The colours of the circles represent the observed values at 130 weather stations. b) Projected changes in the five-year return period driving-rain wind pressure in pascals (expressed as a percentage) from a CanRCM4 ensemble simulation at global warming levels of 1°C (top panel), 2°C (middle panel), and 3°C (bottom panel) relative to the 1986 to 2016 baseline period, corresponding approximately to 2°C, 3°C, and 4°C of warming relative to the pre-industrial era (1850 to 1900). See Chapter 3, Box 3.1, for an explanation of how global warming levels relative to the pre-industrial era translate to increases in Canadian average temperatures over different time periods. Adapted from: Cannon et al. (2020).

8.7.4: Human-perceived heat stress

Key Message 8.14: The intensity and frequency of human-perceived heat stress—which results from the combined effects of high temperatures and humidity—have increased in Canada as a whole (high confidence). Human-perceived heat stress is projected to increase in Canada, driven mainly by increases in air temperature (high confidence).

Globally, urban areas are experiencing more intense heat. With an anticipated 68% of the global population living in cities by 2050 (UN-Habitat, 2022), heat-related risks to human health are expected to increase. In hot and humid conditions, the body’s natural cooling mechanism, sweating, is impeded by higher atmospheric moisture, potentially resulting in adverse health effects and even death due to the compound effects of environmental and internal heat. Projections suggest that human-perceived temperature, typically represented using indices that account for the effects of both air temperature and humidity on heat stress experienced by humans, will increase faster than air temperature throughout the 21st century (Coffel et al., 2018; Fischer and Knutti, 2013; W. Li et al., 2019; Matthews et al., 2017; Russo et al., 2017; Schwingshackl et al., 2021; Scoccimarro et al., 2017).

In Canada, the humidity index (humidex), which considers the combined effects of air temperature and relative humidity on the body, is used as the official measure of human-perceived temperature. A significant increase in human-perceived heat extremes, defined as days with hourly humidex values above 30 (a humidex unit is roughly interpretable as 1°C), has occurred at over 36% of Canadian climatological stations between 1953 and 2012 (É. Mekis et al., 2015). Similarly, a significant increase in days with nighttime hourly humidex values above 20 has been observed at more than 52% of stations, primarily those south of 50°N latitude. Humidex and temperature extremes in western North America, including parts of the western and central provinces and territories of Canada, where the unprecedented June-July 2021 heatwave occurred, have increased from 1940 to 2022, with humidex extremes increasing more rapidly than temperature extremes (Jeong et al., 2023).

Based on CMIP5 simulations, the value of the Temperature Humidity Index (THI), an agroclimatic index that combines temperature and relative humidity to assess environmental stress on animals and plants, is projected to increase by 3 to 4 THI units in agricultural regions in the Prairies between the 1981 to 2010 baseline period and the 2071 to 2100 period under the intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios (Chipanshi et al., 2022). Projections from CMIP6 simulations indicate that regions in northern Canada may start experiencing at least one day per year on average with humidex values above 30°C (Figure 8.26), while southern Ontario, southern Quebec, the Prairies, and the Maritimes may see a significant increase by the end of the century under the very high emissions scenario (SSP5-8.5; Chow et al., 2024). Similar increases in the number of days with a humidex value exceeding 35°C and 40°C are also projected (Chow et al., 2024). Additionally, a large ensemble of CanESM5 projections shows greater increases in human-perceived heat stress than in air temperature across western North America under the low (SSP1-2.6) to very high (SSP5-8.5) emissions scenarios (humidex extreme increases of 4.5−7°C and temperature extreme increases of around 2.9−4.7°C between the 1981−2010 and 2041−2060 periods), emphasizing the rising impact of temperature extremes on human-perceived heat stress (Jeong et al., 2023). These results were extended using a multi-model ensemble of 19 CMIP6 global climate models. At 3°C of global warming relative to the pre-industrial level, the ensemble projected that occurrences of humidex and temperature extremes across western North America that exceed the levels observed during the extreme 2021 heat event would occur every 1.4 and 2.2 years, respectively, increasing to nearly annually at a global warming level of 4°C (Jeong et al., 2024).

Figure take-away: Human-perceived heat stress is projected to increase across Canada, primarily due to rising air temperatures.

Figure title: Historical and projected changes in annual number of days with humidex values greater than 30°C across Canada

Figure 8.26: Annual number of days with humidex values greater than 30°C (HXmax30) across Canada simulated by a multi-model ensemble of 19 CMIP6 models. a) Annual average for the historical 1981 to 2010 period; and the change by 2071 to 2100 relative to 1981 to 2010 under the b) very high emissions scenario (SSP5-8.5) and c) low emissions scenario (SSP1-2.6). The large maps show the ensemble median, and the smaller maps show the 10th and 90th percentiles. Adapted from: Figure 7 in Chow et al. (2024).

8.7.5: Confidence terms in key messages: summary of evidence

Key Message 8.11: The fire season has lengthened in most parts of Canada (high confidence) and is projected to continue to lengthen as the global average temperature increases (high confidence). Fire weather—characterized by hot, dry, and windy conditions conducive to wildfires—has increased in Alberta and British Columbia (medium confidence), with some indications of increases in other regions (very low confidence). The frequency and intensity of extreme fire weather conditions are projected to increase in most regions of Canada as global average temperature rises (high confidence).

Key Message 8.12: Compound coastal flooding arises from jointly occurring climate conditions, such as heavy rain during episodes of extreme water levels. Such co-occurring drivers have increased in frequency and intensity in some parts of Canada, with evidence primarily from Atlantic locations (low confidence), and are projected to become more frequent and intense at locations along the Atlantic, Pacific and Western Arctic coastlines (medium confidence). These projected increases are mostly due to rising sea levels and increases in the frequency and intensity of extreme precipitation events (medium confidence).

Key Message 8.13: There is insufficient evidence to assess historical changes in compound wind and rainfall events in Canada, due to limited studies focusing on observed trends. Compound extreme wind and rainfall events are projected to increase (medium confidence), driven mainly by the greater frequency of future extreme rainfall, but with very low confidence in the regional pattern and magnitude of changes.

Key Message 8.14: The intensity and frequency of human-perceived heat stress—which results from the combined effects of high temperatures and humidity—have increased in Canada as a whole (high confidence). Human-perceived heat stress is projected to increase in Canada, driven mainly by increases in air temperature (high confidence).

Multiple lines of evidence support high confidence in the assessment that the length of the fire season has increased. Increases are found whether the fire season is defined by fire occurrence data or weather proxies. Although the main studies for Canada used data until the end of 2015, another global study considers more recent years. Additionally, event attribution studies for Canadian wildfire seasons also support increased chances of longer fire seasons due to human-caused climate change. Furthermore, a lengthening fire season is consistent with the decreasing snow cover duration discussed in Chapter 6 (section 6.2). There is a strong linkage between warming temperatures and an increase in the fire season length, as warming temperatures lead to conditions warm enough for wildfires to start earlier in the year and end later in the year. This understanding of physical processes supports confidence in both the past and future changes in fire season length.

Multiple globally focused studies using ERA5 reanalysis data have demonstrated increases in fire weather metrics since at least the 1980s in many North American regions, with the strongest trends in the western United States. Significant trends in fire weather metrics were found for some parts of Canada, mostly in the west, but most trends in the rest of the country were not significant. High year-to-year variability hinders the detection of trends. Region-specific studies in Canada, using datasets derived from station observations, identified clear increases in fire weather in Alberta and British Columbia that correspond well to trends in fire activity and area burned. Thus, the increases in those two provinces have medium confidence, but the lack of significant trends and the limited evidence in other regions prevent the assessment of increases in those regions. The understanding of physical processes supports the phenomenon of more days with extreme fire weather caused by warming temperatures. Climate model simulations consistently project increases in fire weather across much of the country, and these increases are stronger under higher levels of global temperature increase. Consistency between physical understanding and multi-model projections leads to high confidence for future increases in fire weather extremes for most of the country. One region in the northwest had more uncertainty in model projections and thus the qualifier “in most regions” was added to the key message.

While there are documented cases where storm surges, extreme precipitation, and high tides have coincided, such as during Hurricane Fiona and other events, particularly along Atlantic coastlines, variability between regions, data limitations, and the limited number of studies lead to low confidence that the intensity and frequency of the climate conditions associated with compound coastal flooding have increased in Canada. Medium confidence in future increases in the frequency and magnitude of compound flooding is supported by global and regional projections linking sea-level rise, more intense precipitation, and declining sea ice to heightened flooding risks. Consistency among global and regional models and strong agreement on these drivers under climate warming bolster this confidence, especially for Canada’s Atlantic and Arctic coasts.

There is insufficient evidence to assess historical changes in compound wind and rainfall events in Canada, due to the limited studies focusing on observed trends. Occurrences of compound wind and rainfall extremes are projected to increase in the future, driven primarily by increases in the frequency of extreme rainfall. The link between warming and the corresponding increase in extreme rainfall (section 8.3.2) and the proportion of precipitation events that occur as rainfall lead to medium confidence that these compound wind and rainfall events will increase. There is nevertheless very low confidence in the pattern and magnitude of the projected changes, due to the large uncertainty in rainfall and wind speed projections owing to multiple factors (Chapter 3; sections 8.3 and 8.4), including those related to changes in atmospheric rivers (Chapter 4, section 4.5) and extratropical cyclones (Chapter 4, section 4.4).

High confidence in the intensification of human-perceived heat stress—the combination of hot and humid conditions—in Canada is supported by historical trends in station data and reanalyses, as well as projections from large, multi-model ensembles of climate simulations. There is high confidence in the projected increase in temperature (Chapter 3), while there is more uncertainty in changes to relative humidity. However, a faster and greater increase in humidex extremes compared to temperature extremes is expected, due to the positive non-linear relationship between humidex and temperature for a given level of relative humidity.

8.8: Synthesis of the effects of warming on climate extremes

Key Message 8.15: Warming has led to increases in the frequency and intensity of hot extremes, human-perceived heat stress, and marine heatwaves, and decreases in cold extremes (high confidence), as well as increases in the frequency and intensity of extreme fire weather conditions and the intensification of heavy precipitation events in Canada (medium confidence), with projections indicating continued and larger changes under higher global warming levels (high confidence).

Climate extremes have changed across Canada and are projected to continue to change with further warming (high confidence). Hot extremes and heat stress have increased in most regions, while cold extremes have decreased (medium to high confidence), mainly due to human influence (high confidence). Extreme precipitation, short-duration rainfall, and heavy snowfall in northern Canada have become more intense and frequent in many areas, and these trends are projected to continue (medium to high confidence). The fire season has lengthened in most regions (high confidence), and fire weather is projected to worsen with rising temperatures (high confidence). Droughts are expected to become more frequent and severe in central and southern Canada (high confidence). Extreme sea-level events and marine heatwaves have increased and are projected to intensify, especially in regions with rising sea levels and declining sea ice (high confidence). Rainfall-related flooding and compound coastal flooding are projected to become more frequent and intense (medium to high confidence).

Warming has led to major changes in climate extremes in Canada over the past century. Observations reveal the increased intensity and frequency of hot extremes and decrease in cold extremes, with cold extremes warming faster than hot extremes due to Arctic amplification. Precipitation extremes have also intensified in Canada as a whole, driven primarily by the increased moisture-holding capacity of the warmer atmosphere. However, regional trends in short-duration precipitation events remain difficult to detect due to the large natural variability and limited station coverage. At scales where changes are detectable, attribution studies consistently highlight the role of human-caused climate change as the primary driver of observed changes in short-duration precipitation extremes. Several high-impact extreme events affecting Canada have been studied to determine whether human influence on the climate had altered their intensity or likelihood of occurrence. Most of these event attribution studies have found that human influence did play a role, including the 2021 western Canada (Pacific Northwest) heatwave, the flooding events in British Columbia (2021) and Ontario and Quebec (2017, 2019), and the 2023 wildfire season.

According to projections, many climate extremes in Canada will intensify under continued global warming. Hot extremes are expected to become more frequent and severe, while cold extremes will continue to diminish. Precipitation extremes, including one-day and multi-day heavy rainfall events, are projected to increase in intensity and frequency, particularly in northern and coastal regions. There is a linear relation between the severity of these changes and global warming levels, highlighting the importance of limiting warming to reduce future risks. Compound events that are strongly affected by warming, either directly or indirectly (for example, through increases in atmospheric moisture), such as concurrent extreme heat and humidity or measures of fire weather risk, are also expected to intensify in the future. While uncertainty still surrounds the precise magnitude of these future changes, particularly for regional and compound events, the agreement between past trends and future projections strengthens confidence in the anticipated effects of climate change on Canada’s climate extremes.

8.9: Key knowledge gaps and emerging issues

Since CCCR2019, the body of climate science literature on weather and climate extremes has grown substantially. Chapter 11 in the IPCC AR6 WGI report (Seneviratne et al., 2021) assessed changes in extremes at the global and regional scales. Similarly, research relevant to Canada, as assessed in this chapter, now offers a clearer picture of the impacts of human-caused climate change on weather and climate extremes in Canada. Key knowledge gaps remain, particularly at the local and regional scales, where adaptation decisions are the most critical. Addressing these gaps is essential to allow the more robust detection and attribution of past changes, improve the reliability of projections, and provide actionable climate information to Canadians.

Limited observation networks are a persistent challenge, particularly in remote and northern regions, where data coverage is sparse. The paucity of long-term, high-quality data hinders the detection of clear historical trends in several types of extremes, including short-duration (sub-daily) rainfall, freezing rain, hail, and wind. While national-scale changes in some variables are clear, the high spatial and temporal variability of many phenomena means that local trends can be obscured by data gaps and inhomogeneities, leading to lower confidence in assessments at the local and regional scales. Strengthening these foundational monitoring networks and the homogeneity of historical data is a critical step toward reducing uncertainty in our understanding of past changes.

Improving projections of extremes requires advancing the capabilities of climate models. While current models provide more confident projections for temperature-related extremes, their relatively coarse spatial resolution limits their ability to explicitly and accurately simulate small-scale physical phenomena, such as convective storms, that are responsible for many high-impact hazards like flash floods, downbursts, hail, and strong winds. Convection-permitting and kilometre-scale climate models, used at regional and global scales, are emerging essential tools capable of capturing these local processes with greater fidelity, offering the potential for more realistic projections of changes in localized weather extremes. However, the high computational cost of these advanced models currently limits our ability to run the large ensembles needed to fully characterize projection uncertainty, a key challenge that must be addressed to make their output standard and widely usable.

Many of Canada’s most damaging weather events are compound in nature, resulting from the interaction of multiple factors in space, time, or both. Fire weather and events such as compound coastal flooding are complex, and their interacting drivers are not fully understood. A key emerging issue is the need to move beyond single-variable analyses and develop integrated physical modelling and statistical analysis frameworks capable of assessing the changing risks of these complex, compound events.

The overall physical understanding of how a warming climate results in more intense and frequent hot extremes and marine heatwaves is relatively clear, as is the thermodynamic link between a warmer atmosphere and the potential for more intense precipitation. The consistency between historical and projected trends in these events strengthens the confidence in our understanding. However, this strong thermodynamic signal is surrounded by greater uncertainty, due to the way in which large-scale atmospheric processes—such as storm tracks, the polar vortex, and North Atlantic hurricanes—evolve and influence Canadian extremes in a warming world. Hence, addressing the key knowledge gaps identified in Chapter 4 is also relevant here.

Since CCCR2019, focus on the analysis of weather and climate extremes has increased, both within Canada and globally. When localized information about changes in extremes is highly uncertain, global or continental information (when available) can still inform our expectations regarding changes in Canadian extremes under increasing global temperatures. With improvements in observational datasets, modelling capabilities, statistical methods, and process understanding, as described above, the assessment of changes in many types of weather and climate extremes can be strengthened. Continued research to close the knowledge gaps outlined here is not an abstract exercise, but rather an essential component of building a more resilient Canada in the face of ongoing climate change.

Frequently Asked Questions

FAQ 8.1: Will all weather and climate extremes get worse in the future?

Not all weather and climate extremes are expected to change in the same way, and whether changes are “worse” depends on the resulting impacts. For example, hot extremes are projected to become more frequent and severe, while cold extremes are expected to diminish (sections 8.2 and 8.8). The impact of these changes on risk depends on exposure and vulnerability (Chapter 10, section 10.4). In terms of health, hotter summers drive a greater incidence of heat-related illness and higher mortality, even as declining cold extremes reduce some winter deaths. However, for ecosystems, warmer winters allow pests such as the mountain pine beetle to survive and potentially expand their range, while hotter, drier conditions lead to increases in fire weather severity. In short, climate change is reshaping Canada’s profile of risk. The impact of some extremes may ease, but many weather and climate hazards that directly harm people, ecosystems, and infrastructure will worsen.

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