Storylines of unprecedented flood risk using reforecast ensemble pooling

Assessing the frequency and impact of large-scale flood events is crucial for improving risk preparedness and emergency management. This effort, however, places emphasis on the need for large data samples to derive statistically robust probabilistic risk estimates. Recent studies have demonstrated the potential of using ensemble climate simulations to drastically extend the sample size of extreme events with respect to the relatively short historical record. This approach has become known as UNSEEN (UNprecedented Simulated Extremes using ENsembles) and has been successfully applied to different types of hazards. In this study, we use ensemble simulations from Copernicus C3S – specifically, seasonal reforecasts from the European Flood Awareness System (EFAS) and ECMWF SEAS5 – to generate a catalogue of flood events that are unprecedented in the historical record, yet plausible in the current climate. We focus on two target regions with recent episodes of severe flooding: The Panaro river basin in northern Italy and the Turia river basin in eastern Spain. We then employ the probabilistic event-based impact model CLIMADA (Aznar-Siguan et al. 2024) to rank the UNSEEN flood events in both basins by their estimated socio-economic impact and develop storylines of unprecedented flood risk. We find that high-impact UNSEEN events generally display recurrent synoptic patterns, similar to those of historical extremes but with higher intensity. Our results suggest that seasonal reforecasts by C3S simulate realistic extremes with respect to historical data following minimal bias correction. This framework can be leveraged to explore risk storylines for stress-testing, and to support the development of mitigation and adaptation strategies to manage disasters.