Paper detail

What do large-scale patterns teach us about extreme precipitation over the Mediterranean at medium- and extended-range forecasts?

Extreme Precipitation Events (EPEs) can have devastating consequences such as floods and landslides, posing a great threat to society and the economy. Predicting such events long in advance can support the mitigation of negative impacts. Here, we focus on EPEs over the Mediterranean, a region that is frequently affected by such hazards. Previous work identified strong connections between localized EPEs and large-scale atmospheric flow patterns, affecting the weather over the entire Mediterranean. We analyze the predictive skill of these patterns in the ECMWF extended-range forecasts and assess if and where these patterns can be used for indirect predictions of EPEs, using the Brier Skill Score. The results show that the ECMWF model provides skillful predictions of the Mediterranean patterns up to 2 weeks in advance. Moreover, using the forecasted patterns for indirect predictability of EPEs outperforms the reference score up to about 10 days lead time for many locations. Especially for high orography locations or coastal areas, like parts of western Turkey, western Balkans, Iberian Peninsula and Morocco this limit extends from 11 to 14 days lead time. This study demonstrates that connections between localized EPEs and large-scale patterns over the Mediterranean extend the forecasting horizon of the model by over 3 days in many locations, in comparison to forecasting based on the predicted precipitation. Thus, it is beneficial to use the predicted patterns rather than the predicted precipitation at longer lead times for EPEs forecasting. The model's performance is also assessed from a user perspective, showing that the EPEs forecasting based on the patterns increases the economic benefits at medium and extended range lead times. Such information could support higher confidence in the decision-making of various users, e.g., the agricultural sector and (re)insurance companies.