Yushi: Unified Precipitation Estimation and Nowcasting from Direct Observations

Precipitation nowcasting is critical for hydrological forecasting and weather-related hazard warning, while most learning-based methods assume that spatially complete precipitation fields are already available as model inputs or prediction targets. In practice, such fields are usually derived from radar through quantitative precipitation estimation (QPE), interpolation, or multi-source merging rather than directly observed. In this paper, we propose Yushi, a unified framework for precipitation estimation and nowcasting directly from radar reflectivity and sparse automatic weather station (AWS) observations. Yushi first learns a gauge-constrained representation in which radar provides dense spatial structure while AWS measurements impose quantitative surface-precipitation constraints. Future precipitation states are then modeled in this latent space using causal conditional flow matching, and an intensity-aware balanced loss transfers precipitation-intensity information to latent flow optimization to emphasize heavy rainfall. Experiments over the Yangtze River Delta region show that Yushi consistently outperforms representative nowcasting baselines across various deterministic and spatially pooled metrics, with particularly clear improvements for intense precipitation. The results demonstrate the potential of directly learning and forecasting precipitation states from complementary primary observations without requiring a pre-constructed gridded precipitation analysis.