Improving subseasonal forecast of precipitation in Europe by combining a stochastic weather generator with dynamical models

We propose a forecasting tool for precipitation based on analogues of circulation defined from 5-day hindcasts and a stochastic weather generator that we call "HC-SWG." In this study, we aim to improve the forecast of European precipitation for subseasonal lead times (from 2 to 4 weeks) using the HC-SWG. We designed the HC-SWG to generate an ensemble precipitation forecast from the European Centre of Medium-range Weather Forecasts (ECMWF) and Centre National de la Recherche M & eacute;t & eacute;orologique (CNRM) subseasonal-to-seasonal ensemble reforecasts. We define analogues from 5-day ensemble reforecast of Z500 from the ECMWF (11 members) and CNRM (10 members) models. Then, we generate a 100-member ensemble for precipitation over Europe. We evaluate the skill of the ensemble forecast using probabilistic skill scores such as the continuous ranked probability skill score (CRPSS) and receiver operating characteristic curve. We obtain reasonable forecast skill scores within 35 days for different locations in Europe. The CRPSS shows positive improvement with respect to climatology and persistence at the station level. The HC-SWG shows a capacity to distinguish between events and non-events of precipitation within 15 days at the different stations. We compare the HC-SWG forecast with other precipitation forecasts to further confirm the benefits of our method. We found that the HC-SWG shows improvement against the ECMWF precipitation forecast until 25 days. We show the capacity of a statistical system to improve the forecast skill of European precipitation using the analogues of subseasonal-to-seasonal dynamical models. Our results show the high potential of the circulation analogue method to improve ensemble forecasts of European precipitation compared with numerical models. image