RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours

We present a deep learning model for high-resolution probabilistic precipitation forecasting over an 8-hour horizon in Europe, overcoming the limitations of radaronly deep learning models with short forecast lead times. Our model efficiently integrates multiple data sources -including radar, satellite, and physics-based numerical weather prediction (NWP) -while capturing long-range interactions, resulting in accurate forecasts with robust uncertainty quantification through consistent probabilistic maps. Featuring a compact architecture, it enables more efficient training and faster inference than existing models. Extensive experiments demonstrate that our model surpasses current operational NWP systems, extrapolationbased methods, and deep-learning nowcasting models, setting a new standard for high-resolution precipitation forecasting in Europe, ensuring a balance between accuracy, interpretability, and computational efficiency. Code is available at https://github.com/rafapablos/RainPro .