In this article, we present extreme value statistics of temperature extremes over continental France with a focus on their implications for electrical power infrastructure. These are obtained by fitting a non-stationary generalised extreme value distribution using a Bayesian setup, which also provides errors or uncertainty values for all estimates. Within this method, a combination of simulated data from a collection of 28 CMIP6 models and measured records from the E-OBS dataset is used. The investigated climate scenarios are SSP2-4.5, SSP3-7.0 and SSP5-8.5, considering both historical and future climates spanning the years from 1850 to 2099. The method provides full spatial resolution on a 0.25 degree grid, allowing to asses extreme temperatures at arbitrary locations. Leveraging this particular aspect, various maps revealing the spatial structure of annual maximum high temperature extremes over continental France for the median and the statistical upper bound are shown together with summary information for the administrative regions in France towards the end of the century. It is statistically possible that the south west region of Occitanie could reach temperatures of up to 57°C under a high emission scenario by 2080, which is the highest compared to all other regions in continental France. We also perform a comparison to the reference climate adaptation trajectory for France (TRACC - Trajectoire de réchauffement de Référence pour l'Adaptation au Changement Climatique), showing that it potentially underestimates the stated maximum temperatures which could be surpassed by up to +8°C. Furthermore, five reference electrical power infrastructure locations are investigated on how they are potentially affected by temperature extremes with the quantified intensities.

Scientific and media attention to extreme events has increased in recent years due to their severity and the damage they cause. Estimating the contribution of climate change to the probability of occurrence and intensity of these events has become a common practice, thanks to initiatives relying on climate science researchers, who diagnose the return period of the event and the contribution of the anthropogenic forcings in the event properties. These initiatives have succeeded in shedding light on the detection and attribution concept but still rely on the commitment of researchers to deal with an ever-increasing number of events. Here, we propose a proof of concept of an automatic attribution method which, in a few seconds, can attribute a heat wave that has occurred anywhere on the globe and estimate its statistical properties both in the past and in the future, using an existing method based on Bayesian statistics that combines past observations and climate models. Our web application currently covers extreme heat events over a period of 3 days and paves the way to a number of relevant applications, such as (i) enabling public or private organizations to become autonomous, i.e., processing the events they wish without waiting for an academic research investigation; (ii) handling events that have received less attention but have had a major impact, particularly in developing countries; and (iii) climate monitoring of extreme events over a specific region. SIGNIFICANCE STATEMENT: Quantifying the contribution of climate change to the frequency and intensity of extreme events (e.g., heat waves, floods) is an ad hoc exercise, often associated with an academic publication. Here, we propose a new paradigm via an automatic attribution method that, in a few seconds, provides key indicators regarding the contribution of anthropogenic climate change to the properties of hot extreme events that has occurred anywhere on the globe. This approach and its online application enable scientists and stakeholders trained in its use to study the events that interest them and to conduct climate monitoring of past events by refining the statistical indicators as more observations become available.

Within the Explore2 national project, a new set of bias-corrected regional climate projections sub-sampled from the EURO-CORDEX (EUR11) ensemble has been produced to describe the impact of climate change on water resources and to support impact studies over mainland France. This dataset has been specially selected to reflect the expected changes in temperature and precipitation of the complete EURO-CORDEX (EUR11) ensemble while taking those of CMIP6 into account as consistency constraint. Yet, the selection allows to obtain a smaller ensemble size to handle with. The process of GCM/RCM couples selection is fully described in the article. The dataset makes it possible to characterize and partition the various sources of uncertainty about the evolution of the climate in France, by taking into account three greenhouse gas emission scenarios (RCP 2.6, RCP 4.5 and RCP 8.5), multiple regional climate models (allowing to dispose to 9 to 17 GCM/RCM couples depending on the emission scenario), two methods of statistical bias correction (ADAMONT and CDF-t) and continuous time series to explore internal variability.This dataset contains 10 climate variables at daily resolution, enabling the calculation of a very large number of climate impact indicators, as well as its use to drive a wide variety of hydrological models in France. Examples of climate change representations suitable for this dataset are provided for cumulative precipitation at seasonal scale. These representation methods are intended to guide potential users of this data when aiming to characterize the robustness of the changes (according to individual simulations, time horizons or climate change scenarios) and to identify contrasting scenarios across a territory. A narrative approach is also proposed to facilitate the exploration of individual projections of climate change, allowing for a more accurate consideration of inter-annual variability and extremes Four narratives were selected among the 17 GCM/RCM couples in collaboration with hydrologists which correspond to contrasting changes of temperature and precipitation in order to reflect a plurality of contrasting possible climate futures within the dispersion of the Explore2–2022 dataset.The richness of this dataset and the inclusion of the most recent regional climate simulations for France justified its use in constructing and illustrating the reference warming trajectory for climate change adaptation (TRACC) in France, backed by the 3rd National Climate Change Adaptation Plan.The Explore2 project worked to build a data-set meeting the FAIR Data Principles1 to maximize transparency, easiness and re-usability of data.

Heat waves can have devastating impacts on societies and ecosystems. Their frequencies and intensities are increasing globally with anthropogenic climate change. Statistical models using extreme value theory (EVT) have been used for quantifying the risks of extreme temperatures, but recent very intense events have cast doubt on their ability to represent the tail probabilities of temperatures. Using outputs from a large ensemble of a climate model, we show that physics-based estimates of the upper bound of temperatures in the midlatitudes are 3 degrees-8 degrees C higher than suggested by EVTbased models. We propose a new method to bridge the gap between the physical and statistical estimates by forcing the EVT-based models to have an upper bound coherent with the bound provided by the instability of the air column. We show that our method reduces the underestimation of tail risks while not deteriorating the performance of the statistical models on the core of the distribution of extreme temperatures.

Multi-scenario, multi-model ensembles of hydrological projections are widely used to describe possible futures of regional hydrology and inform adaptation strategies. The Explore2 dataset is such an ensemble of river flow projections in Metropolitan France. It provides future simulations for 1735 catchments with modeling chains composed of different hydrological models forced by 36 regional climate projections based on bias-adjusted EUROCORDEX simulations. This study assesses the uncertainties of this ensemble with QUALYPSO, a method specifically designed to deal with incomplete ensembles and to disentangle and quantify all uncertainty sources, including that due to internal variability.Focusing on results obtained at the end of the century, this study shows a strong agreement between modeling chains towards decreases in low flows in a large southern part of France for a high-emission scenario, and very uncertain changes for the annual mean and high flows. Emission scenario uncertainty is the dominant source of uncertainty for low flows over the whole of France, and for mean annual flows in southeastern France. The contribution of the global and regional climate models is important for mean and high flows, especially in rainfall-dominated areas. Regional climate models contribute considerable uncertainty to low flows, much more than global models. The contribution of hydrological model uncertainty is large for low flows, moderate for mean annual flows, and small for high flows. For all climate and hydrological indicators, internal variability is often large and cannot be overlooked. It is often of the same order and sometimes larger than the uncertainty on the climate change response.

We describe an improved method and the associated package for estimating the statistics of temperature extremes in a Bayesian framework. Building on previous work, this method uses a range of climate model simulations to provide a prior of the real-world changes, and then considers observations to derive a posterior estimate of past and future changes. The new version described in this study makes it possible to process several scenarios simultaneously, while keeping one single counterfactual world (i.e., the world without human influence). We offer a free licensed, easy-to-use command-line tool called ANKIALE (ANalysis of Klimate with bayesian Inference: AppLication to extreme Events), which can be used to reproduce the analyses presented here, as well as to process user-defined events. ANKIALE is based on a python code, but is designed to be used from the command line interface. ANKIALE is natively parallel, enabling it to be used on a personal computer as well as on a supercomputer. To derive the posterior, ANKIALE uses state of art MCMC-methods to sample the posterior distribution. The potential of this method and tool is illustrated via an application to maximum temperature over Europe between 1850 and 2100 (the posterior is derived from ERA5, covering the period from 1940 to 2024), at a 0.25 degrees resolution, for a range of four emission scenarios, including a particular focus on the city of Paris (France).

As the climate warms, cold waves are expected to become less intense and less frequent. Is there still a risk of reliving events comparable to the most intense cold spells we can remember? We analyze four remarkable cold spells that have occurred since 2010 in different regions: Western Europe, Texas, China, Brazil. We show that all these recent events have a moderate to high probability of not happening again by 2100 – typically 50% to 90% in an intermediate emissions scenario, depending on the event. The probabilities are even higher for iconic events of the 20th century or earlier. Our results suggest that the most intense cold snaps, and their associated icy landscapes in mid-latitude regions, are disappearing or have already disappeared due to anthropogenic climate change.

Extreme event attribution methodologies have been proposed to estimate the impacts of anthropogenic global warming on observed climatological and meteorological extremes. The classical risk-based approach uses extreme value theory (EVT) to derive changes in the unconditional probabilities of yearly maxima but bears the risk of comparing events with different dynamical mechanisms. The flow analogue method is a conditional attribution method which compares events with similar synoptic-scale dynamics. Here we propose a procedure for estimating both the conditional intensity change and the probability ratio of observed extreme events with this method. We illustrate the procedure on three recent extreme events in Europe and compare the results obtained to the EVT-based approach. We show that the conditional flow analogue method tends to give more significant results for these events, which suggests a stronger climate change signal than the one detected with the unconditional approach.

Reaching a surface temperature of 50 degrees C in a heavily populated region, like Paris, would have devastating effects. Although such a high value seems far from the present-day record of 42.6 degrees C, its occurrence cannot be dismissed by the end of the 21st century, due to the continuous increase of global mean temperature. In this paper, we address two questions that were asked by the City of Paris to a group of scientists: When does this event start to be likely? What are the prevailing meteorological conditions? We base our study on the CMIP6 simulation ensemble. Many of the CMIP6 yield biases in temperature. Rather than using methods of bias correction, which are not necessarily adapted to high extremes, we propose a pragmatic approach of model selection in order to seek such high temperature events that are deemed realistic. We analyze the meteorological conditions leading to first occurrences of such hot events and their common atmospheric patterns. This paper describes a simple data mining approach (on a large ensemble of climate model simulations) which could be adapted to other regions of the world, in order to help decision makers anticipating and adapting to such devastating meteorological events.

CDSupdate is a Python package that automates the process of retrieving, processing, and managing climate data from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). The tool generates daily climate data summaries, performs calculations to create custom variables such as relative humidity and heat index which serve as risk assessments, and organizes the data into a user-friendly format. By simplifying data retrieval and performing on-the-fly calculations, it saves users valuable time and effort, enabling more focus on data analysis and interpretation.

The Summer Olympic Games in 2024 will take place during the apex of the temperature seasonal cycle in the Paris Area. The midlatitudes of the Northern hemisphere have witnessed a few intense heatwaves since the 2003 epitome event. Those heatwaves have had environmental and health impacts, which often came as surprises. In this paper, we search for the most extreme heatwaves in Ile-de-France that are physically plausible, under climate change scenarios, for the decades around 2024. We apply a rare event algorithm on CMIP6 data to evaluate the range of such extremes. We find that the 2003 record can be exceeded by more than 4°C in Ile-de-France before 2050, with a combination of prevailing anticyclonic conditions and cut-off lows. This study intends to build awareness on those unprecedented events, against which our societies are ill-prepared. Those results could be extended to other areas of the world.

. In early April 2021 several days of harsh frost affected central Europe. This led to very severe damages in grapevine and fruit trees in France, in regions where young leaves had already unfolded due to unusually warm temperatures in the preceding month (march 2021. We analysed with observations and 172 climate model simulations how human-induced climate 20 change affected this event over central France, where many vineyards are located. We found that, without human-caused climate change, such temperatures in April or later in spring would have been even lower by 1.2°C [0.75°C;1.7°C]. However, climate change also caused an earlier occurrence of bud burst, that we characterized in this study by a growing-degree-day index value. This shift leaves young leaves exposed to more winter-like conditions with lower minimum temperatures and longer nights, an effect that over-compensates the

Abstract The Summer Olympic Games in 2024 will take place during the apex of the temperature seasonal cycle in the Paris Area. The midlatitudes of the Northern hemisphere have witnessed a few intense heatwaves since the 2003 event [1]. Those heatwaves have had environmental and health impacts, which often came as surprises [2]. In this paper, we search for the most extreme heatwaves in Ile-de-France that are physically plausible, under climate change scenarios, for the decades around 2024. We circumvent the sampling limitation by applying a rare event algorithm [3] on CMIP6 data [4] to evaluate the range of such extremes. We find that the 2003 record can be exceeded by more than 4◦C in Ile-de-France before 2050, with a combination of prevailing anticyclonic conditions and cut-off lows. This study intends to build awareness on those unprecedented events, against which our societies are ill-prepared. Those results could be extended to other areas of the world.

The Summer Olympic Games in 2024 will take place during the apex of the temperature seasonal cycle in the Paris Area. The mid-latitudes of the Northern hemisphere have witnessed a few intense heatwaves since the 2003 event. Those heatwaves have had environmental and health impacts, which often came as surprises. In this paper, we search for the most extreme heatwaves in Ile-de-France that are physically plausible, under climate change scenarios, for the decades around 2024. We circumvent the sampling limitation by applying a rare event algorithm on CMIP6 data to evaluate the range of such extremes. We find that the 2003 record can be exceeded by more than 4 °C in Ile-de-France before 2050, with a combination of prevailing anticyclonic conditions and cut-off lows. This study intends to raise awareness of those unprecedented events, against which our societies are ill-prepared, in spite of adaptation measures designed from previous events. Those results could be extended to other areas of the world.

Abstract. Bias correction and statistical downscaling are now regularly applied to climate simulations to make then more usable for impact models and studies. Over the last few years, various methods were developed to account for multivariate – inter-site or inter-variable – properties in addition to more usual univariate ones. Among such methods, temporal properties are either neglected or specifically accounted for, i.e., differently from the other properties. In this study, we propose a new multivariate approach called “Time Shifted Multivariate Bias Correction” (TSMBC), which targets to correct the temporal dependency in addition to the other marginal and multivariate aspects. TSMBC relies on considering the initial variables at various times (i.e., lags) as additional variables to correct. Hence, temporal dependencies (e.g., auto-correlations) to correct are viewed as inter-variable dependencies to be adjusted and an existing multivariate bias correction (MBC) method can then be used to answer this need. This approach is first applied and evaluated on synthetic data from a Vector Auto Regressive (VAR) process. In a second evaluation, we work in a “perfect model” context where a Regional Climate Model (RCM) plays the role of the (pseudo-) observations, and where its forcing Global Climate Model (GCM) is the model to be downscaled/bias corrected. For both evaluations, the results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted. However, increasing too much the number of lags to consider does not necessarily improve the temporal properties and a too strong increase in the number of dimensions of the dataset to correct can even imply some potential instability in the adjusted/downscaled results, calling for a reasoned use of this approach for large datasets.

We describe a statistical method to derive event attribution diagnoses combining climate model simulations and observations. We fit nonstationary Generalized Extreme Value (GEV) distributions to extremely hot temperatures from an ensemble of Coupled Model Intercomparison Project phase 5 (CMIP) models. In order to select a common statistical model, we discuss which GEV parameters have to be nonstationary and which do not. Our tests suggest that the location and scale parameters of GEV distributions should be considered nonstationary. Then, a multimodel distribution is constructed and constrained by observations using a Bayesian method. This new method is applied to the July 2019 French heatwave. Our results show that both the probability and the intensity of that event have increased significantly in response to human influence. Remarkably, we find that the heat wave considered might not have been possible without climate change. Our results also suggest that combining model data with observations can improve the description of hot temperature distribution.

Over the first half of 2020, Siberia experienced the warmest period from January to June since records began and on the 20th of June the weather station at Verkhoyansk reported 38 °C, the highest daily maximum temperature recorded north of the Arctic Circle. We present a multi-model, multi-method analysis on how anthropogenic climate change affected the probability of these events occurring using both observational datasets and a large collection of climate models, including state-of-the-art higher-resolution simulations designed for attribution and many from the latest generation of coupled ocean-atmosphere models, CMIP6. Conscious that the impacts of heatwaves can span large differences in spatial and temporal scales, we focus on two measures of the extreme Siberian heat of 2020: January to June mean temperatures over a large Siberian region and maximum daily temperatures in the vicinity of the town of Verkhoyansk. We show that human-induced climate change has dramatically increased the probability of occurrence and magnitude of extremes in both of these (with lower confidence for the probability for Verkhoyansk) and that without human influence the temperatures widely experienced in Siberia in the first half of 2020 would have been practically impossible.

Nous proposons dans cet article d'appliquer une méthode d'attribution au changement climatique, pour la canicule de juillet 2019 sur la France métropolitaine. Nous décrivons d'abord comment l'attribution est effectuée. Ensuite, nous montrons qu'aujourd'hui le risque d'occurrence de ce type de canicule a été multiplié par au moins 10 et qu'en 2040 il sera multiplié par au moins 20. De plus, nous analysons le changement de risque à la fin du siècle pour deux scénarios climatiques : dans le meilleur des cas, nous reviendrons à la situation actuelle en 2100 ; dans un cas défavorable, des canicules similaires à celle de 2019 produiront des températures d'au moins 43 °C sur la moitié de la France. We propose in this article to apply a method of attribution to climate change for the French Metropolitan heat wave of July 2019. We first describe how the attribution is performed. Then, we show that currently the risk of occurrence of this type of heat wave has been multiplied by at least 10, that in 2040 it will be multiplied by at least 20. In addition, we analyze the change in risk at the end of the century for two climate scenarios: in the best case we return to the current situation in 2100, in an unfavourable case heat waves similar to the one in 2019 will produce temperatures of at least 43 °C over half of France.