PrecMamba: State-Space Forecasting and Flow-Matching Ensembles for Extreme Precipitation

Precipitation nowcasting provides essential short-range forecasts that serve both meteorological science and everyday public safety. Deep learning models for precipitation nowcasting are commonly trained with pixel-wise regression objectives. On sparse, heavy-tailed precipitation fields, conventional objectives tend to produce blurred predictions and suppress the heavy-rain events that matter most. We introduce PrecMamba, a two-stage precipitation forecasting framework in which the deterministic stage learns more than the centre of the intensity distribution, letting the generative stage focus on the remaining uncertainty. The first stage employs a Mamba-based deterministic backbone to model the large-scale spatio-temporal evolution of precipitation, trained with a multi-quantile pinball loss that penalises the under-prediction of high-intensity events. The second stage models the residual around the deterministic forecast with conditional flow matching, generating sharp and diverse ensemble members in a few integration steps. Experiments on SEVIR, MeteoNet, Shanghai Radar and CIKM show that PrecMamba achieves the highest critical success index on all four benchmarks, and on SEVIR it is also best at the most intense threshold.