While weather forecasts are used for the warning of flood events and climate projections for long-term climate adaptation measures, it is the knowledge of the coming months that is crucial for the management and control of water reservoirs. Furthermore, especially in the context of global climate change, it is assumed that the occurrence of exceptional hydrometeorological conditions like droughts will increase in the future. This will have significant implications on various sectors including the hydropower sector. Stakeholders, water managers, and decision makers therefore have to prepare for these changing conditions through climate-proofing, i.e. by considering and adapting to the past, present, and future availability of freshwater resources. Since several years, data providers like the European Centre for Medium-Range Weather Forecasts (ECMWF) develop seasonal forecast products with forecast horizons up to 12 months. These products have the potential to significantly improve the planning and management of e.g. hydropower generation during the coming months. But global datasets like the seasonal ECMWF SEAS5 forecasts are often not directly applicable for the regional water management. While the coarse spatial resolution of 35 km and more cannot represent e.g. convective precipitation events especially in regions with complex terrain, global products often suffer from highly variable spatial and temporal performances. These performances both depend on the forecasted period and the forecast horizon, i.e. the time span between the date when the forecast was issued and the forecasted dates.
In this study, we therefore assess the performance and reliability for the latest ECMWF seasonal forecasts and provide a benchmark for the quality of such state-of-the-art datasets for the water management. The global data is analysed in terms of accuracy (mean absolute error skill score), overall performance (continuous ranked probability skill score), sharpness (interquantile range skill score), reliability, and several spatial performance measures. In order to assess the regional transferability of our findings and to evaluate the potential for improving the regional water management, we assess these metrics over several semi-arid target regions, in which the energy supply highly depends on hydropower generation. These include the river basins of the Rio São Francisco (Brazil), the Karun (Iran), or the Atbara and Blue Nile (Sudan). We also demonstrate how global seasonal forecasts can be adapted to regional applications. For improving the spatial resolution, we assess multiple parameterisations of the Weather Research and Forecasting (WRF) model in each regions. As these approaches require huge computational effort which complicates the application for longer periods and for large numbers of ensemble forecasts, we also analyse the performance of statistical bias correction and spatial disaggregation (BCSD) methods for ensuring statistical consistency between the global products and regional reference information. This also allows us to use the regionalised information for the prediction and monitoring of exceptional hydrometeorological events like, e.g., droughts. Overall, we present the current state-of-the-art of global seasonal forecasts as well as a framework for improving the applicability as decision support for the regional water management and, in particular, climate-proofing of the hydropower sector in the context of global climate change.