Seasonal forecasts for Germany: Enhancing the predictive capability of global SEAS5 ensemble forecasts using bias correction

For an optimized use of water resources for irrigation or power generation, knowledge of their expected availability in the coming months is essential. This particular sub-seasonal to seasonal time horizon is covered by seasonal forecasting systems like SEAS5 from the European Centre for Medium-Range Weather Forecasts (ECMWF), which could provide crucial information for an improved and more timely water management. In this study, we evaluate the skill of precipitation and temperature forecasts from SEAS5 for Germany. The performance of forecasts without any post-processing or bias-correction remains below the climatology from the second lead month. To increase the performance, we apply a post-processing approach for Bias Correction and Spatial Disaggregation (BCSD) to a) increase the spatial resolution and b) reduce biases compared to our chosen reference ERA5-Land. By means of several quality parameters such as the Continuous Ranked Probability Skill Score (CRPSS) and the Brier Skill Score (BSS), it is shown that the corrected SEAS5 seasonal forecasts at monthly resolution deliver a significantly increased performance compared to purely climatological forecasts and raw SEAS5 forecasts, especially in the first forecast month. Special focus is put on climatic extremes, since especially here the seasonal forecasts have the potential to provide highly valuable information that are, by definition, absent, e.g., in climatological forecasts. This is clearly evident for compound events, which show increased predictability up to five months in advance. Months with normal conditions perform rather poorly, whereas abnormally warm or dry months are well forecasted up to and including the sixth lead month. Temperature variables perform particularly well, while precipitation forecasts show lower skill. Forecasts of the Standardized Precipitation Evapotranspiration Index (SPEI, a widely used indicator for droughts and, hence, limited water availability) show higher skill than pure precipitation forecasts. We further assess the performance of post-processed SEAS5 forecasts in terms of the Potential Economic Value (PEV), which allows for the quantification of economic savings due to forecast-based actions. Depending on a well-chosen cost-loss-ratio of particular actions, seasonal forecasts from SEAS5 show a promising skill for timely decision making in the water management and related sectors. In our presentation, we hence demonstrate the skill of post-processed seasonal forecasts from SEAS5 over Germany and provide a benchmark for other forecasting products and post-processing routines.