IMALCast: Intensity-Aware Multimodal Alignment Learning for Heavy-Rainfall Nowcasting

Accurate precipitation nowcasting, especially for extreme rainfall events, plays a vital role in supporting decision-making across multiple sectors of society. However, pointwise regression objectives commonly used in deep learning models are not well aligned with threshold-sensitive operational verification, particularly for high-intensity precipitation regions. Geostationary satellite observations offer complementary cloud information for precipitation forecasting, but differences in spatial and temporal resolution complicate their integration with radar data. To address these challenges, we propose IMALCast, an intensity-aware multimodal alignment learning framework for deterministic precipitation nowcasting. For objective alignment, IMALCast integrates weighted intensity regression with global and high-intensity structural losses motivated by threshold-based verification. For observation alignment, IMALCast uses dual-tower encoding and cross-modal fusion to preserve and integrate modality-specific radar and satellite information. Experiments on the nationwide China Radar–Satellite Dataset and the public SEVIR benchmark demonstrate strong forecasting performance. In particular, IMALCast achieves up to a 65.7% relative improvement in the regional-scale critical success index for heavy rainfall over the strongest baseline in our comparison.