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

ICLR 2026 Conference Submission17561 Authors

Keywords: Precipitation Forecasting, Probabilistic Forecasting, High-Resolution Forecasting

TL;DR: We introduce a deep learning model for high-resolution probabilistic precipitation forecasting in Europe, integrating radar, satellite, and NWP data to improve accuracy, uncertainty quantification, and computational efficiency over 8-hour forecasts.

Abstract: We present a deep learning model for high-resolution probabilistic precipitation forecasting over an 8-hour horizon in Europe, overcoming the limitations of radar-only 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, extrapolation-based 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.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 17561