Predictive performance of bias-corrected seasonal SEAS5 forecasts for Germany

For an optimized use of water resources for irrigation or power generation, knowledge about their expected availability in the coming months is essential. The European Centre for Medium-Range Weather Forecasts (ECMWF) issues monthly ensemble forecasts for the next seven months (SEAS5 model). In order to be able to use these for Germany, we present an analysis of the temperature and precipitation forecast quality for Germany with a resolution on district level. ERA5 reanalysis data from ECMWF and raster observation data from the German Weather Service are used as a basis for comparison. To increase the performance, two bias corrections were tested, a mean value correction and a correction of both the mean value and the standard deviation. Using several quality parameters such as the Continuous Ranked Probability Skill Score (CRPSS), the Brier Skill Score (BSS) and the Receiver Operating Characteristic (ROC) curves, it is shown that the uncorrected SEAS5 seasonal forecasts at monthly resolution provide a significantly increased performance compared to purely climatological forecasts, mainly in the first Lead Month. The reliability (RV) averages 78% across years, months, regions, and variables, with the first forecast month providing a particularly good RV of 86%. All prediction months have positive CRPSS for the precipitation at all times for all regions, and the bias correction to the mean also improves the skill by an average of 33% based on the CRPSS. In addition, both bias corrections significantly improve the prediction quality of the above-average extremes (> 90th percentile) for temperatures and precipitation. Thus, the SEAS5 seasonal forecasts, together with subsequent bias correction procedures, open up the possibility of estimating expected water availability at the county level with forecast periods of several months.