Forecasting extreme precipitation in the central Mediterranean: Different products for different timescales

Extreme precipitation events pose a great threat to society, the economy, and the environment with devastating consequences like floods and landslides. Improved forecasting of this hazard, combined with adequate Early Warning Systems, can thus support the mitigation of the associated negative impacts. Here, we propose new forecasting products that substantially improve predictions of extreme precipitation events at different timescales, from short- to medium- and extended-range forecasts. Our study focuses on forecasting extreme rainfall in Calabria, south Italy. This region, located in the central Mediterranean, has a complex and abruptly varying topography that poses additional challenges in precipitation formation and forecasting. For this work we use data from three sources; observational precipitation from the EOBS dataset, modelled atmospheric fields from ERA5 reanalysis, and forecasted atmospheric fields from ECMWF reforecasts for 1 up to 45 days lead time. Based on our analysis we show that different forecasting horizons require different forecasting products. More specifically, bias-correcting the precipitation forecasts for short- to medium-range lead times provides the most informative outputs to end-users. For extended-range forecasts though, this method is not sufficient, as it cannot outperform the reference score based on climatological information. For such extended lead times, it is beneficial to use the large-scale weather variability to infer reliable information about extreme precipitation. This can be accomplished by connecting Mediterranean-wide weather patterns, with the probability of extreme precipitation in Calabria. We present the benefits of the methods, based on long-term statistical analysis using a range of indicators, such as the Brier skill score, and the reliability diagram.