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This study analyzes the uncertainty of seasonal (winter and summer) precipitation extremes as simulated by a recent version of the Canadian Regional Climate Model (CRCM) using 16 simulations (1961–1990), considering four sources of uncertainty from: (a) the domain size, (b) the driving Atmosphere–Ocean Global Climate Models (AOGCM), (c) the ensemble member for a given AOGCM and (d) the internal variability of the CRCM. These 16 simulations are driven by 2 AOGCMs (i.e. CGCM3, members 4 and 5, and ECHAM5, members 1 and 2), and one set of re-analysis products (i.e. ERA40), using two domain sizes (AMNO, covering all North America and QC, a smaller domain centred over the Province of Québec). In addition to the mean seasonal precipitation, three seasonal indices are used to characterize different types of variability and extremes of precipitation: the number of wet days, the maximum number of consecutive dry days, and the 95th percentile of daily precipitation. Results show that largest source of uncertainty in summer comes from the AOGCM selection and the choice of domain size, followed by the choice of the member for a given AOGCM. In winter, the choice of the member becomes more important than the choice of the domain size. Simulated variance sensitivity is greater in winter than in summer, highlighting the importance of the large-scale circulation from the boundary conditions. The study confirms a higher uncertainty in the simulated heavy rainfall than the one in the mean precipitation, with some regions along the Great Lakes—St-Lawrence Valley exhibiting a systematic higher uncertainty value.

Short-duration precipitation extremes are widely used in the design of engineering infrastructure systems and they also lead to high impact flash flood events and landslides. Better understanding of these events in a changing climate is therefore critical. This study assesses characteristics of short-duration precipitation extremes of 1-, 3-, 6- and 12-h durations in terms of the precipitation-temperature (P–T) relationship in current and future climates for ten Canadian climatic regions using the limited area version of the global environment multiscale (GEM) model. The GEM simulations, driven by ERA-Interim reanalysis and two coupled global climate models (CanESM2 and MPI-ESM), reproduce the general observed regional P–T relationship characteristics in current climate (1981–2010), such as sub-CC (Clausius–Clapeyron) and CC scalings for the coastal and northern, and inland regions, respectively, albeit with some underestimation. Analysis of the transient climate change simulations suggests important shifts and/or extensions of the P–T curve to higher temperature bins in future climate (2071–2100) for RCP4.5 and 8.5 scenarios, particularly for 1-h duration. Analysis of the spatial patterns of dew point depression (temperature minus dew point temperature) and convective available potential energy (CAPE) corresponding to short-duration precipitation extremes for different temperature bins show their changing relative importance from low to high temperature bins. For the low-temperature bins, short-duration precipitation extremes are largely due to high relative humidity, while for high-temperature bins, strong convection due to atmospheric instability brought by surface warming is largely responsible. The analysis thus addresses some of the key knowledge gaps related to the behavior of P–T relationship and associated mechanisms for the Canadian regions.

Abstract Few records of spring paleoclimate are available for boreal Canada, as biological proxies recording the beginning of the warm season are uncommon. Given the spring warming observed during the last decades, and its impact on snowmelt and hydrological processes, searching for spring climate proxies is receiving increasing attention. Tree‐ring anatomical features and intra‐annual widths were used to reconstruct the regional March to May mean air temperature from 1770 to 2016 in eastern boreal Canada. Nested principal component regressions calibrated on 116 years of gridded temperature data were developed from one Fraxinus nigra and 10 Pinus banksiana sites. The reconstruction indicated three distinct phases in spring temperature variability since 1770. Ample phases of multi‐decadal warm and cold springs persisted until the end of the Little Ice Age (1850–1870 CE) and were gradually replaced since the 1940s by decadal to interannual variability associated with an increase in the frequency and magnitude of warm springs. Significant correlations with other paleotemperature records, gridded snow cover extent and runoff support that historical high flooding were associated with late, cold springs with heavy snow cover. Most of the high magnitude spring floods reconstructed for the nearby Harricana River also coincided with the lowest reconstructed spring temperature per decade. However, the last 40 years of observed and reconstructed mean spring temperature showed a reduction in the number of extreme cold springs contrasting with the last few decades of extreme flooding in the eastern Canadian boreal region. This result indicates that warmer late spring mean temperatures on average may contribute, among other factors, to advance the spring break‐up and to likely shift the contribution of snow to rain in spring flooding processes.

The convection-permitting climate model (CPCM), WRF-ARW at 4 km resolution, is able to capture the observed relationships between precipitation extremes and temperature (PT scaling) in western Canada. By analyzing the CPCM simulated PT scalings, we found they have robust patterns at different percentiles of precipitation intensity and even between the current and future climate. This is due to the stable annual cycle of the regional climate. The PT scaling pattern is physically governed by the amount of water vapour and the ascending velocity of air. Approximately 95% of the precipitation intensity variation can be explained by the vertical velocity and precipitable water in western Canada. The PT scaling for the current climate does not tell how precipitation extremes would response to a warmer climate. Trend scaling theory was utilized to estimate the intensification of precipitation extremes in a warmer climate. It shows that, in western Canada, the coast is particularly vulnerable to precipitation extremes under global warming. Precipitation extremes are projected to increase at a super Clausius-Clapeyron (CC) scale over the coast, approximately at a CC scale over the prairies and mountains, and a sub-CC scale over the northern region. The warming effect on precipitation extremes is even stronger when the concept of”wet-day trend scaling” is introduced.

In the context of global warming, the Clausius–Clapeyron (CC) relationship has been widely used as an indicator of the evolution of the precipitation regime, including daily and sub-daily extremes. This study aims to verify the existence of links between precipitation extremes and 2 m air temperature for the Ottawa River Basin (ORB, Canada) over the period 1981–2010, applying an exponential relationship between the 99th percentile of precipitation and temperature characteristics. Three simulations of the Canadian Regional Climate Model version 5 (CRCM5), at three different resolutions (0.44°, 0.22°, and 0.11°), one simulation using the recent CRCM version 6 (CRCM6) at “convection-permitting” resolution (2.5 km), and two reanalysis products (ERA5 and ERA5-Land) were used to investigate the CC scaling hypothesis that precipitation increases at the same rate as the atmospheric moisture-holding capacity (i.e., 6.8%/°C). In general, daily precipitation follows a lower rate of change than the CC scaling with median values between 2 and 4%/°C for the ORB and with a level of statistical significance of 5%, while hourly precipitation increases faster with temperature, between 4 and 7%/°C. In the latter case, rates of change greater than the CC scaling were even up to 10.2%/°C for the simulation at 0.11°. A hook shape is observed in summer for CRCM5 simulations, near the 20–25 °C temperature threshold, where the 99th percentile of precipitation decreases with temperature, especially at higher resolution with the CRCM6 data. Beyond the threshold of 20 °C, it appears that the atmospheric moisture-holding capacity is not the only determining factor for generating precipitation extremes. Other factors need to be considered, such as the moisture availability at the time of the precipitation event, and the presence of dynamical mechanisms that increase, for example, upward vertical motion. As mentioned in previous studies, the applicability of the CC scaling should not be generalised in the study of precipitation extremes. The time and spatial scales and season are also dependent factors that must be taken into account. In fact, the evolution of precipitation extremes and temperature relationships should be identified and evaluated with very high spatial resolution simulations, knowing that local temperature and regional physiographic features play a major role in the occurrence and intensity of precipitation extremes. As precipitation extremes have important effects on the occurrence of floods with potential deleterious damages, further research needs to explore the sensitivity of projections to resolution with various air temperature and humidity thresholds, especially at the sub-daily scale, as these precipitation types seem to increase faster with temperature than with daily-scale values. This will help to develop decision-making and adaptation strategies based on improved physical knowledge or approaches and not on a single assumption based on CC scaling.

Although numerous studies have looked at the long-term trend of the temporal variability of winter temperature and precipitation in southern Quebec, no study has focused on the shifts in series means and the dependence between these two types of climate variables associated with this long-term trend. To fill these gaps, we used the Lombard method to detect the shifts in mean values and the copula method to detect any change in dependence between extreme (maximum and minimum) temperatures and precipitation (snow and rain) over the periods 1950–2000 (17 stations) and 1950–2010 (7 stations). During these two periods, the shifts in mean values of temperature and precipitation were recorded at less than half of the stations. The only significant change observed at the provincial scale is a decrease in the amount of snowfall, which occurred in many cases during the 1970s. This decrease affected stations on the north shore (continental temperate climate) more strongly than stations on the south shore (maritime temperate climate) of the St Lawrence River. However, this decrease in the amount of snowfall had no impact on the dependence over time between temperature and precipitation as snow.

In this study future flooding frequencies have been estimated for the Grand River catchment located in south - western Ontario, Canada. Historical and future climatic projections made by fifteen Coupled Model Inter - comparison Project - 3 climate models are bias - corrected and downscaled before they are used to obtain mid - and end of 21 st century streamflow projections. By comparing the future projected and historically observed precipitation and temperature record s it is found that the mean and extreme temperature events will intensify in future across the catchment. The increase is more drastic in the case of extreme events than the mean events. The sign of change in future precipitation is uncertain. Further flow extremes are expected to increase in magnitude and frequency in future across the catchment. The confidence in the projection is more for low return period (<10 years) extreme events than higher return period (10 - 100 years) events. It can be expected that increases in temperature will play a dominant role in increasing the magnitude of low return period flooding events while precipitation seems to play an important role in shaping the high return period events.

Among natural-disaster risks, heat waves are responsible for a large number of deaths, diseases and economic losses around the world. As they will increase in severity, duration and frequency over the decades to come within the context of climate change, these extreme events constitute a genuine danger to human health, and heat-warning systems are strongly recommended by public health authorities to reduce this risk of diseases and of excessive mortality and morbidity. Thus, evidence-based public alerting criteria are needed to reduce impacts on human health before and during persistent hot weather conditions. The goal of this guide is to identify alert thresholds for heat waves in Canada based on evidence, and to propose an approach for better defining heat waves in the Canadian context in order to reduce the risks to human health and contribute to the well-being of Canadians. This guide is the result of the collaboration among various research and public institutions working on: 1) meteorological and climate aspects, i.e. the Meteorological Service of Canada (MSC, Environment and Climate Change Canada), and the ESCER centre at the Universite du Quebec a Montreal, and 2) public health, i.e. Health Canada and the Institut National de Sante Publique du Quebec.

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affected by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. These results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.

Ce travail de recherche a pour objectif d’évaluer le risque d’inondation printanier à Rigaud (Québec, Canada), en faisant une analyse de cas de l’inondation historique du printemps 2017. Dans la première partie de ce mémoire, une analyse des conditions météorologiques printanières dans le bassin versant de la rivière des Outaouais (BVRO) est d’abord réalisée à partir des données météorologiques sous forme de grille (DAYMET) à 1 km de résolution (1980 à 2017), mais également à partir des données météorologiques de stations d’Environnement et Changement climatique Canada (1963 à 2017). La sévérité des aléas d’inondation à Rigaud (1963 à 2019) est ensuite évaluée en termes d’ampleur de l’aléa hydrologique et des dommages potentiels associés. Dans la seconde partie de ce mémoire,l’exposition au risque d’inondation à Rigaud ainsi que les conditions socio-environnementales contribuant à exacerber le risque d’inondation à l’exutoire du bassin sont caractérisées. Une analyse multicritère qui tient compte de la pente, de la capacité de drainage des sols et de l’utilisation du sol en plus des caractéristiques de l’aléa météorologique dans le BVRO permet d’estimer la contribution des sous-bassins versants (SBV) à l’inondation printanière de 2017 à Rigaud. Au printemps 2017, le dégel brusque du début avril ainsi que le caractère exceptionnellement intense et régulier des événements de précipitation liquide aux mois d’avril et mai, généralisés sur l’ensemble du BVRO, ont contribué en partie à la sévérité de l’inondation. Ces facteurs météorologiques ont eu des conséquences importantes sur l’occurrence et l’intensité de l’inondation durant ces mois, d’autant plus que les conditions les plus extrêmes se sont produites dans les SBV les plus près de l’exutoire et les plus vulnérables, compte tenu de leurs fortes pentes et des modifications importantes au territoire engendrées par les activités humaines entre 1990 et 2010. L’indice de sévérité révèle que les inondations de 2017 et de 2019 se distinguent des autres inondations majeures en raison de l’intensité des débits journaliers enregistrés à l’exutoire du BVRO sur une durée de plus de 40 jours, alors que les dernières inondations historiques de 1974 et 1976 ont plutôt enregistré des niveaux d’eau records à l’exutoire du bassin. À Rigaud, l’exposition au risque d’inondation s’est également accrue entre 1970 et 2017, en raison du développement de l’aménagement périurbain (infrastructure et construction résidentielle) au cours de ces années, résultant en un potentiel de dommages beaucoup plus important lors des événements récents de 2017 et 2019. _____________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : inondation, risque d’inondation, aléa, facteurs de risque, conditions socioenvironnementales, conditions hydrométéorologiques, exposition, bassin versant de la rivière des Outaouais, Rigaud

Abstract The objective of this study is to compare the spatiotemporal variability of seasonal daily mean flows measured in 17 watersheds, grouped into three homogeneous hydroclimatic regions, during the period 1930–2023 in southern Quebec. With regard to spatial variability, unlike extreme daily flows, seasonal daily mean flows are very poorly correlated with physiographic factors and land use and land cover. In fall, they are not correlated with any physiographic or climatic factor. In winter, they are positively correlated with the rainfall and winter daily mean maximum temperatures. In spring, they are strongly correlated positively with the snowfall but negatively with the spring daily mean maximum temperatures. However, in summer, they are better correlated with forest area and, to a lesser extent, with the rainfall. As for their temporal variability, the application of six different statistical tests revealed a general increase in daily mean flows in winter due to early snowmelt and increased rainfall in fall. In summer, flows decreased significantly in the snowiest hydroclimatic region on the south shore due to the decrease in the snowfall. In spring, no significant change in flows was globally observed in the three hydroclimatic regions despite the decrease in the snowfall due to the increase in the rainfall. In fall, flows increased significantly south of 47°N on both shores due to the increase in the rainfall. This study demonstrates that, unlike extreme flows, the temporal variability of seasonal daily average flows is exclusively influenced by climatic variables in southern Quebec. Due to this influence, seasonal daily mean flows thus appear to be the best indicator for monitoring the impacts of changes in precipitation regimes and seasonal temperatures on river flows in southern Quebec.

Precipitation and temperature are among major climatic variables that are used to characterize extreme weather events, which can have profound impacts on ecosystems and society. Accurate simulation of these variables at the local scale is essential to adapt urban systems and policies to future climatic changes. However, accurate simulation of these climatic variables is difficult due to possible interdependence and feedbacks among them. In this paper, the concept of copulas was used to model seasonal interdependence between precipitation and temperature. Five copula functions were fitted to grid (approximately 10 km × 10 km) climate data from 1960 to 2013 in southern Ontario, Canada. Theoretical and empirical copulas were then compared with each other to select the most appropriate copula family for this region. Results showed that, of the tested copulas, none of them consistently performed the best over the entire region during all seasons. However, Gumbel copula was the best performer during the winter season, and Clayton performed best in the summer. More variability in terms of best copula was found in spring and fall seasons. By examining the likelihoods of concurrent extreme temperature and precipitation periods including wet/cool in the winter and dry/hot in the summer, we found that ignoring the joint distribution and confounding impacts of precipitation and temperature lead to the underestimation of occurrence of probabilities for these two concurrent extreme modes. This underestimation can also lead to incorrect conclusions and flawed decisions in terms of the severity of these extreme events.

Extreme events are widely studied across the world because of their major implications for many aspects of society and especially floods. These events are generally studied in terms of precipitation or temperature extreme indices that are often not adapted for regions affected by floods caused by snowmelt. The rain on snow index has been widely used, but it neglects rain-only events which are expected to be more frequent in the future. In this study, we identified a new winter compound index and assessed how large-scale atmospheric circulation controls the past and future evolution of these events in the Great Lakes region. The future evolution of this index was projected using temperature and precipitation from the Canadian Regional Climate Model large ensemble (CRCM5-LE). These climate data were used as input in Precipitation Runoff Modelling System (PRMS) hydrological model to simulate the future evolution of high flows in three watersheds in southern Ontario. We also used five recurrent large-scale atmospheric circulation patterns in north-eastern North America and identified how they control the past and future variability of the newly created index and high flows. The results show that daily precipitation higher than 10&thinsp;mm and temperature higher than 5&thinsp;∘C were necessary historical conditions to produce high flows in these three watersheds. In the historical period, the occurrences of these heavy rain and warm events as well as high flows were associated with two main patterns characterized by high Z500 anomalies centred on eastern Great Lakes (HP regime) and the Atlantic Ocean (South regime). These hydrometeorological extreme events will still be associated with the same atmospheric patterns in the near future. The future evolution of the index will be modulated by the internal variability of the climate system, as higher Z500 on the east coast will amplify the increase in the number of events, especially the warm events. The relationship between the extreme weather index and high flows will be modified in the future as the snowpack reduces and rain becomes the main component of high-flow generation. This study shows the value of the CRCM5-LE dataset in simulating hydrometeorological extreme events in eastern Canada and better understanding the uncertainties associated with internal variability of climate.