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For investigating, assessing and anticipating climate change, tens of Global Climate Models (GCM)s have been designed, each modeling the Earth system slightly differently. To extract a robust signal from the diverse simulations and outputs, models are typically gathered into multi-model ensembles (MMEs). Those are then summarised in various ways, including (possibly weighted) multi-model means, medians or quantiles. In this work, we introduce a new probability aggregation method termed "alpha-pooling" which builds an aggregated Cumulative Distribution Function (CDF) designed to be closer to a reference CDF over the calibration (historical) period. The aggregated CDFs can then be used to perform bias adjustment of the raw climate simulations, hence performing a "multi-model bias correction". In practice, each CDF is first transformed according to a non-linear transformation that depends on a parameter a. Then, a weight is assigned to each transformed CDF. This weight is an increasing function of the CDF closeness to the reference transformed CDF. Key to the a-pooling is a parameter α that describes the type of transformation, and hence the type of aggregation, generalising both linear and log-linear pooling methods. We first establish that a-pooling is a proper aggregation method verifying some optimal properties. Then, focusing on climate models simulations of temperature and precipitation over Western Europe, several experiments are run in order to assess the performance of a-pooling against methods currently available, including multi-model means and weighted variants. A reanalyses-based evaluation as well as a perfect model experiment and a sensitivity analysis to the set of climate models are run. Our findings demonstrate the superiority of the proposed pooling method, indicating that α-pooling presents a robust and efficient way to combine GCMs' CDFs. The results of this study also show that the CDFs pooling strategy for "multi-model bias correction" is a credible alternative to usual GCM-by-GCM bias correction methods, by allowing to handle and consider several climate models at once. [ABSTRACT FROM AUTHOR]

In this study, we aim to assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities of Western Europe. The SWG is based on random sampling of analogs of the geopotential height at 500 hPa. The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill score (continuous rank probability skill score and correlation) for lead times of 5 and 10 days for different areas in Europe. We found that the low predictability of our model is related to specific weather regimes, depending on the European region. Comparing SWG forecasts to ECMWF forecasts, we found that the SWG shows a good performance for 5 days. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g. region for analogs) must be tuned for each region in order to optimize its performance. [ABSTRACT FROM AUTHOR]