Abstract: Assessing the impact of precipitation anomalies during the growing season on crop yields helps mitigate climatic risks in the cultivation of winter wheat. Based on daily precipitation data and provincial yield records in Henan Province from 1980 to 2019, the responses of winter wheat yield to precipitation anomaly at different growth stages were identified using first-order differencing. TOPSIS method was then employed to construct a rainfall disaster intensity index (RDII) for critical periods. The trend and periodic characteristics of RDII in different regions were diagnosed by K-means clustering, M-K test, and ensemble empirical mode decomposition. The early-warning effectiveness of circulation factors was evaluated by combining time-lagged Spearman correlation analysis with a classification discriminant model. The results showed that climate-induced yield variations were more sensitive to extreme precipitation during the milking ripening period, with the RDII constructed by multi-factor explaining 28% of its fluctuation. Spatially, RDII during critical periods exhibited a significant decreasing trend from south to north. Sub-regional RDII generally showed a non-significant weakening trend and high-frequency oscillations with a primary cycle of 2-3 years. A linear discriminant model driven by circulation factors from the preceding 12 months demonstrated relatively high predictive accuracy for rainfall disaster years in regions such as south-central Henan.

Key words: yield, winter wheat, extreme precipitation, rainfall disaster intensity index, circulation factor