Deep Learning‐Based Postprocessing to Enhance Subseasonal Soil Moisture Forecasts Over Europe

Abstract Accurate forecasts on subseasonal (S2S) timescales are essential for the preparation and mitigation of the impacts of high‐impact events, such as flash droughts. To improve the accuracy of soil moisture forecasts—a critical factor in identifying flash droughts—we present a hybrid modeling framework that combines dynamical forecasts from the European Centre for Medium‐Range Weather Forecasts with deep learning (DL) models. This approach not only corrects biases in numerical weather prediction models but also improves spatial resolution, increasing the accuracy of S2S forecasts. By using deterministic inputs, such as the ensemble mean and spread, we further assess the uncertainty of forecasts through dropout neural networks via Monte Carlo sampling. Our results demonstrate that the DL models outperform baseline methods, offering skillful S2S forecasts of soil moisture. This advanced hybrid framework provides more accurate soil moisture predictions, ultimately supporting improved strategies for managing and mitigating the impacts of flash droughts.