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 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.

@article{sarabia2025_2505.10271,
  title={ RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours },
  author={ Rafael Pablos Sarabia and Joachim Nyborg and Morten Birk and Jeppe Liborius Sjørup and Anders Lillevang Vesterholt and Ira Assent },
  journal={arXiv preprint arXiv:2505.10271},
  year={ 2025 }
}