DETEX – Detection of Extreme Precipitation Events in Present and Future Climates at High Resolution Using Machine Learning

We present a machine learning based method for predicting extreme precipitation events. This method uses dynamical and thermodynamical variables at coarse resolution as input and the probability of extreme precipitation at higher resolution as the ground truth. Preliminary results show that our detection method, trained on historical EC-Earth3 global climate data and an extreme precipitation mask calculated from the 99th percentile of precipitation from the HCLIM regional model, achieves an accuracy of over 90% for the 2050–2100 period under the SSP126 and SSP370 scenarios within the European domain.
We are working on further improving the method, testing its performance on reanalysis datasets (e.g., ERA5 and CERRA), and adapting it for statistical downscaling and regional climate model emulation.