RainCast: A High-Resolution 72-Hour Short-Term Precipitation Forecasting Model

Abstract

Accurate short-term precipitation forecasting, which targets predictions up to 3 days ahead, has long been challenging, mainly due to extreme sample imbalance and complex multiscale physical processes. Existing regression- or classification-based deep learning methods often produce over-smoothed precipitation fields, lack physical guidance, and provide limited capability for uncertainty quantification, while radar-based nowcasting methods are unsuitable for multi-day forecasting. To address these limitations, we propose RainCast, a high-resolution framework for hourly precipitation forecasting over China up to 72 hours ahead at 0.05° resolution. RainCast incorporates two key designs: (1) a physics-guided feature extractor, which emulates continuity-equation-based diagnostics to extract vorticity, divergence, and vertical-motion-related signals from circulation fields; and (2) a multi-head output design that supports both deterministic forecasts with a regression head and probabilistic multi-member forecasts with an ensemble head, thereby reducing over-smoothing. Experiments show that RainCast consistently outperforms baselines. For heavy rainfall events (50 mm/24 h), RainCast improves CSI by up to 62.82% over GFS, improves CRPS by 19.35% over GEFS. And interpretability analysis further identifies 600-hPa temperature as a key signal for East Asian precipitation forecasting, likely related to hydrometeor phase transitions near the freezing–melting layer.