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Ensembles used for probabilistic weather forecasting tend to be biased and underdispersive. This paper proposes a statistical method for postprocessing ensembles based on quantile regression forests (QRF), a generalization of random forests for quantile regression. This method does not fit a parametric probability density function (PDF) like in ensemble model output statistics (EMOS) but provides an estimation of desired quantiles. This is a nonparametric approach that eliminates any assumption on the variable subject to calibration. This method can estimate quantiles using not only members of the ensemble but any predictor available including statistics on other variables. The method is applied to the Météo-France 35-member ensemble forecast (PEARP) for surface temperature and wind speed for available lead times from 3 up to 54 h and compared to EMOS. All postprocessed ensembles are much better calibrated than the PEARP raw ensemble and experiments on real data also show that QRF performs better than EMOS, and can bring a real gain for human forecasters compared to EMOS. QRF provides sharp and reliable probabilistic forecasts. At last, classical scoring rules to verify predictive forecasts are completed by the introduction of entropy as a general measure of reliability.

In the context of climate change, assessing how likely a particular change or event was caused by human influence is important for mitigation and adaptation policies. In this work we propose an extreme event attribution (EEA) methodology to analyze yearly maxima records, key indicators of climate change that spark off media attention and research in the EEA community. Although they deserve a specific statistical treatment, algorithms tailored to record analysis are lacking. This is particularly true in a non‐stationary context. This work aims at filling this methodological gap by focusing on records in transient climate simulations. We apply our methodology to study records of yearly maxima of daily precipitation issued from the numerical climate model IPSL‐CM6A‐LR. Illustrating our approach with decadal records, we detect in 2023 a clear human induced signal in half the globe, with probability mostly increasing, but decreasing in the south and north Atlantic oceans. Plain Language Summary The increase of frequency and strength of climate extremes raises the interest in quantifying the extent to which these changes are influenced by climate change. In this work we propose an Extreme Event Attribution (EEA) methodology allowing us to assess whether climate records are attributable to climate change. Records have been typically studied by considering climate unvarying in some time span, despite the fact that climate is constantly changing. This work aims at filling this methodological gap by focusing on records in time‐varying climate simulations. We apply our methodology to study records of yearly maxima of daily precipitation issued from the latest version of the Institute Pierre Simon Laplace climate model. Illustrating our approach with decadal records, we detect in 2023 a clear human induced signal in almost half of the globe. Even though decadal record probability mostly increases, we observe a decrease of record probability in the south and north Atlantic oceans. Key Points This work proposes a simple definition of non‐stationary records and offers a method to assess the likelihood of record event changes We analyze annual maxima of daily precipitation, whose statistical features strongly depart from a Gaussian probability distribution IPSL‐CM6A‐LR climate model highlights that by 2023 signals of rainfall yearly maxima decadal records have emerged on the half of the globe

A comparison of moderate to extreme daily precipitation from the ERA‐5 reanalysis by the European Centre for Medium‐Range Weather Forecasts against two observational gridded data sets, EOBS and CMORPH, is presented. We assess the co‐occurrence of precipitation days and compare the full precipitation distributions. The co‐occurrence is quantified by the hit rate. An extended generalized Pareto distribution (EGPD) is fitted to the positive precipitation distribution at every grid point and confidence intervals of quantiles compared. The Kullback–Leibler divergence is used to quantify the distance between the entire EGPDs obtained from ERA‐5 and the observations. For days exceeding the local 90th percentile, the mean hit rate is 65% between ERA‐5 and EOBS (over Europe) and 60% between ERA‐5 and CMORPH (globally). Generally, we find a decrease of the co‐occurrence with increasing precipitation intensity. The agreement between ERA‐5 and EOBS is weaker over the southern Mediterranean region and Iceland compared to the rest of Europe. Differences between ERA‐5 and CMORPH are smallest over the oceans. Differences are largest over NW America, Central Asia, and land areas between 15°S and 15°N. The confidence intervals on quantiles are overlapping between ERA‐5 and the observational data sets for more than 80% of the grid points on average. The intensity comparisons indicate an excellent agreement between ERA‐5 and EOBS over Germany, Ireland, Sweden, and Finland, and a disagreement over areas where EOBS uses sparse input stations. ERA‐5 and CMORPH precipitation intensity agree well over the midlatitudes and disagree over the tropics. Key Points The timing and the intensity of daily precipitation are assessed over Europe and globally Extended generalized Pareto distributions are fitted to precipitation from ERA‐5 and station and satellite data Agreement between data sets is highest in the midlatitudes

Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the r-year return level. We present a method for producing maps of precipitation return levels and uncertainty measures and apply it to a region in Colorado. Separate hierarchical models are constructed for the intensity and the frequency of extreme precipitation events. For intensity, we model daily precipitation above a high threshold at 56 weather stations with the generalized Pareto distribution. For frequency, we model the number of exceedances at the stations as binomial random variables. Both models assume that the regional extreme precipitation is driven by a latent spatial process characterized by geographical and climatological covariates. Effects not fully described by the covariates are captured by spatial structure in the hierarchies. Spatial methods were improved by working in a space with climatological coordinates. Inference is provided by a Markov chain Monte Carlo algorithm and spatial interpolation method, which provide a natural method for estimating uncertainty.

The results of probabilistic event attribution studies depend on the choice of the extreme value statistics used in the analysis, particularly with the arbitrariness in the selection of appropriate thresholds to define extremes. We bypass this issue by using the Extended Generalized Pareto Distribution (ExtGPD), which jointly models low precipitation with a generalized Pareto distribution and extremes with a different Pareto tail, to conduct daily precipitation attribution across the contiguous United States (CONUS). We apply the ExtGPD to 12 general circulation models from the Coupled Model Intercomparison Project Phase 6 and compare counterfactual scenarios with and without anthropogenic emissions. Observed precipitation by the Climate Prediction Center is used for evaluating the GCMs. We find that greenhouse gases rather than natural variability can explain the observed magnitude of extreme daily precipitation, especially in the temperate regions. Our results highlight an unambiguous linkage of anthropogenic emissions to daily precipitation extremes across CONUS. Plain Language Summary We investigate how human‐induced emissions affect daily rainfall extremes across the United States. The attribution of an extreme event to human‐induced emissions depends on the selected extreme event statistics, with setting a threshold to define what counts as an extreme event remaining a major challenge. To overcome this, we used the Extended Generalized Pareto Distribution (ExtGPD) that jointly models both low and heavy rainfall events without defining a threshold, providing a more complete picture of the full distribution including extremes. We fitted the ExtGPD to 12 general circulation models and compared scenarios with and without human‐induced emissions. Our findings suggest that human emissions are responsible for the observed intensity of daily rainfall extremes across the United States, especially in regions with temperate climates, and that these extremes would have been smaller without greenhouse gases. Key Points We apply the Extended Generalized Pareto Distribution for probabilistic event attribution to bypass issues with threshold specification Anthropogenic emissions alone could exacerbate the observed magnitude of extreme daily precipitation across the United States The study underscores the urgent need for mitigation, revealing a clear link between anthropogenic activities and extreme precipitation

To satisfy a wide range of end users, rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We introduce local statistical postprocessing methods based on quantile regression forests and gradient forests with a semiparametric extension for heavy-tailed distributions. These hybrid methods make use of the forest-based outputs to fit a parametric distribution that is suitable to model jointly low, medium, and heavy rainfall intensities. Our goal is to improve ensemble quality and value for all rainfall intensities. The proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France using the Météo-France ensemble prediction system called Prévision d’Ensemble ARPEGE (PEARP). They are verified with a cross-validation strategy and compete favorably with state-of-the-art methods like analog ensemble or ensemble model output statistics. Our methods do not assume any parametric links between the variables to calibrate and possible covariates. They do not require any variable selection step and can make use of more than 60 predictors available such as summary statistics on the raw ensemble, deterministic forecasts of other parameters of interest, or probabilities of convective rainfall. In addition to improvements in overall performance, hybrid forest-based procedures produced the largest skill improvements for forecasting heavy rainfall events.

The evaluation of precipitation extremes is a paramount challenging issue in climate sciences and there is a need of both assessing changes in climate projections and comparing climate model simulations with observations. To address these needs, a non-parametric approach specifically designed for extremes is here proposed. The method is tested and applied to observations and CMIP5 historical simulations and future projections (under the high emission scenario RCP8.5) over the Euro-Mediterranean region. Results support the existence of a scaling relationship among models and between models and observations in terms of conditional mean of the extremes. However, the rescaled tails of models' precipitation show significant differences when compared with observations. Concerning future projections, models show an intensification of heavy precipitation (especially at the end of the 21st century) linked to a change in the conditional mean of extremes. More complex changes in the upper tails are not identified at the mid-century, while a lack of model agreement prevents drawing definitive conclusions for the end of the century.

High‐impact climate damages are often driven by compounding conditions, such as elevated heat stress arising from combined high humidity and temperatures. To explore future changes in compounding hazards under several climate scenarios, climate emulators can provide light‐weight, data‐driven complements to Earth System Models (ESMs). Yet, only a few existing emulators jointly emulate multiple climate variables. We introduce MERCURY (Multi‐resolution EmulatoR for CompoUnd climate Risk analYsis), a spatio‐temporal, multi‐resolution emulator designed for compound climate risk analysis. MERCURY employs image‐compression‐based techniques for memory‐efficient emulation and consists of two main modules. The regional module represents the monthly, regional response of a given variable to yearly Global Mean Temperature using a probabilistic additive model, resolving regional cross‐correlations. The resulting regional values are then jointly disaggregated to grid‐cell level values using a lifting‐scheme operator, founded on principles of Discrete Wavelet Transforms. We demonstrate MERCURY on the humid‐heat metric, wet bulb globe temperature (WBGT), as derived from temperature and relative humidity emulations. The emulated WBGT spatial correlations correspond well to those of ESMs and the 95%$\\%$and 97.5%$\\%$quantiles of WBGT distributions are well captured, with an average of 5%$\\%$deviation. MERCURY's setup allows for region‐specific emulations from which one can efficiently “zoom” into the grid‐cell level across multiple variables by means of the reverse lifting‐scheme operator. This circumvents the traditional problem of having to emulate complete, global‐fields of climate data and resulting storage requirements. Plain Language Summary Climate model emulators are approximations of climate models that provide a quick and low‐cost alternative to exploring future climate scenarios. Traditional emulators generate large amounts of data covering the whole world, which still need to be condensed when exploring local and regional impacts. In this paper, we propose a new emulator based off image compression techniques. The setup allows one to “zoom” in and out from global to regional to local levels, providing user‐relevant information across scales. It furthermore conserves both large‐scale and local features and can be run in minutes. Given its versatile framework, the approach is easily extendable to new variables, and in this paper we demonstrate its ability to jointly capture temperature and relative humidity. Key Points Climate model emulators traditionally generate global fields amounting to Peta‐Bytes of data We introduce the emulator MERCURY, that employs a lifting scheme based on image compression techniques to emulate region‐specific fields whilst conserving global characteristics The lifting scheme further reduces data dimensionality allowing versatile extension to additional climate variables at minimal cost

The influence of climate change on mid-latitudes atmospheric circulation is still very uncertain. The large internal variability makes it difficult to extract any statistically significant signal regarding the evolution of the circulation. Here we propose a methodology to calculate dynamical trends tailored to the circulation of specific days by computing the evolution of the distances between the circulation of the day of interest and the other days of the time series. We compute these dynamical trends for two case studies of the hottest days recorded in two different European regions (corresponding to the heat-waves of summer 2003 and 2010). We use the NCEP reanalysis dataset, an ensemble of CMIP5 models, and a large ensemble of a single model (CESM), in order to account for different sources of uncertainty. While we find a positive trend for most models for 2003, we cannot conclude for 2010 since the models disagree on the trend estimates.