Research and cross-basin validation of a large model-assisted early warning method based on precipitation analysis in the Yangtze River Basin

To address the limitations of existing basin-scale precipitation risk identification and early-warning information generation, particularly in regional spatial coherence assessment and intelligent text generation, this study proposes a precipitation risk monitoring method for the Yangtze River Basin by integrating a station-level composite risk index, a regional spatial coherence index, and large language model-based warning generation. The method was developed using 3-hourly multivariate meteorological observations collected at 100 representative stations across the Yangtze River Basin from March 3 to 16, 2026, and was applied to analyze precipitation characteristics in the upper, middle, and lower reaches of the basin. The results show that precipitation activity was most pronounced in the middle reaches of the Yangtze River Basin and was accompanied by high humidity, weak winds, and low visibility. Precipitation in the upper reaches was dominated by scattered and intermittent events, while precipitation in the lower reaches was generally weak. Based on the distinct precipitation and environmental characteristics of the upper, middle, and lower reaches, the proposed method calculates station-level composite risk indices using region-specific weighting schemes and derives a regional coherence index by incorporating reach-specific weights. Through dynamic weight adjustment, the station-level composite risk index and regional coherence index jointly characterize the intensity, persistence, and spatial synchrony of precipitation risk, thereby enabling the effective identification of local high-risk stations and the spatial organization of risk at the regional scale. On this basis, a large language model generates structured warning messages corresponding to the monitoring indicators and their assessment results. An extended test in the Yellow River Basin indicates that the proposed method has a certain degree of cross-basin transferability and application potential. These findings provide a useful reference for basin-scale precipitation risk monitoring and intelligent early warning, as well as technical support for enhancing meteorological disaster prevention and mitigation capabilities across river basins.