HurdleCast: Hurdle-Gated Mixture of Experts for Extreme Precipitation Nowcasting

Recent AI weather forecasting models have achieved substantial gains in forecast skill across lead times ranging from hours to weeks. Nonetheless, record-breaking extremes remain a persistent challenge, with far less consistent improvements in predictive performance. This limitation is especially consequential for precipitation nowcasting, where short-lead forecasts support severe-weather warning, flash-flood management, and dam operations, and extremes pose the greatest operational risk. Even modern generative nowcasting models achieve strong forecast quality on light-to-moderate precipitation but struggle to predict extremes. High-intensity extremes are sparsely represented in training data, while AI models typically optimize an average loss across the full precipitation range. We hypothesize that modeling this distribution through a single shared set of generative dynamics limits the representation of rare, high-intensity events. We introduce HurdleCast, a flow-matching generative model that explicitly decomposes precipitation prediction into occurrence and conditional severity through a supervised mixture-of-experts velocity field. Two probabilistic gates route latent flow-matching dynamics among experts specialized for precipitation intensity attributes, implemented here as dry, moderate, and heavy precipitation. On SEVIR and MeteoNet, HurdleCast improves over state-of-the-art AI nowcasting models at extreme precipitation intensities, increasing Critical Success Index (CSI) at the highest intensity thresholds by 11–30% and 9–31%, respectively.