Towards seasonal prediction of extreme temperature indices

The variability of meteorological extreme events in Europe is strongly affected by climate change. In particular, it has been shown that temperature extremes have increased in frequency, duration and intensity, becoming one of the natural disasters with the most severe socio-economic impacts for European communities. As climate change continues, seasonal forecasts represent a valuable tool for predicting upcoming high-risk climate conditions in advance (up to several months) to support decision-makers in the implementation of preventive measures. Recently, several studies assessed the predictive skill of decadal climate forecasts, while only few investigations evaluated the ability of climate forecasts in predicting climate extremes at seasonal time scale. This study analyses the performances of the German Climate Forecast System (GCFS) in forecasting selected temperature extreme indices, including the number of hot days and warm spells, which are key for the evaluation of health and mortality risks. The skill of the GCFS is estimated for Germany using a statistically downscaled bias-corrected hindcast-ensemble system with high spatial resolution (5 km) for the period between 1990 and 2020 at daily time scale. Forecasts of the climate indices for the summer months are evaluated for different lead-months (i.e., subintervals of the forecast period) using metrics such as the anomaly correlation coefficient and the ranked probability skill score. To this aim, we compare the hindcast ensemble with two daily observational datasets: ERA5 (9 km spatial resolution) and HYRAS (5 km regular grid covering the German drainage basins, including headwaters in the neighbouring countries). This analysis contributes to an improved understanding of the performances of seasonal forecast systems in order to effectively support decision-makers adopting proper risk-mitigation actions against climate extremes impacts.