Multi-Scale Truncated Diffusion with Horizon-Targeted Reforecasting for Precipitation Nowcasting

Precipitation nowcasting aims to forecast the distribution of rainfall over the upcoming 0–2 hours based on historical spatio-temporal sequences. However, current mainstream methods still struggle to accurately capture the evolutionary characteristics of precipitation, mitigate the progressive degradation of forecast accuracy with increasing lead time, and adapt to complex mountainous terrains. To address these challenges, we propose a multi-scale truncated diffusion with horizon-targeted reforecasting for precipitation nowcasting. Specifically, a multi-scale truncated diffusion module is designed to jointly model macroscopic precipitation evolution and fine-grained local structures through multi-scale spatio-temporal guidance, while shortening the reverse diffusion trajectory. Subsequently, a horizon-targeted reforecasting module is introduced to selectively reforecast the terminal frames, which are more susceptible to accumulated errors at longer lead times, thereby improving late-horizon forecast accuracy. Furthermore, to enhance precipitation forecasting capabilities in complex mountainous terrains, we construct the Southern Anhui Mountain Radar Dataset. Extensive experiments conducted on our self-constructed dataset, alongside two public datasets (SEVIR and CIKM), demonstrate that our method achieves state-of-the-art performance across all evaluation metrics, enabling robust precipitation nowcasting under complex mountainous conditions.