Ensemble forecast of extreme precipitation in Europe by combining a stochastic weather generator with dynamical models

Ensemble precipitation forecasts with sub-seasonal lead times provide useful information for decision-makers when they sufficiently sample the possible outcomes of trajectories. In this study, we present a forecasting tool for extreme precipitation ensemble forecast over Europe using a stochastic weather generator (SWG) based on analogs of the atmospheric circulation. This approach is tested for sub-seasonal lead times (from 2 to 4 weeks) to forecast European precipitation and temperature as well as the Madden Jullian Oscillation (Krouma et al, 2022,2023). SWG ensemble forecasts yield promising probabilistic skill scores for shorter and sub-seasonal timescales for precipitation (Krouma et al., 2022,2024) as well as for temperature (Yiou and Déandréis, 2019).

An updated version of the SWG, HC-SWG forecasting tool (HC refers to Hindcast and SWG to the stochastic weather generator) based on a combination of dynamical and stochastic models, was used to forecast European precipitation for the sub-seasonal lead time (Krouma et al., 2024, in review, QJRMS). The HC-SWG is based on analogs of the S2S model of the ECMWF and CNRM ensemble members 5 days ahead. We obtained reasonable forecast skill scores at the station level with respect to climatology. And we found that the HC-SWG shows improvement against the ECMWF precipitation forecast until 25 days.

In this work, we aim to use the HC-SWG to generate an ensemble of 100 members for extreme precipitation over Europe at the station level (Stockholm, Madrid, Paris..). We evaluate the ensemble forecast of the HC-SWG and we compare the HC-SWG forecast with other precipitation extreme forecasts to further confirm the advantage of our method.