Research on the reciprocal feedback relationship and influencing factors between meteorological and agricultural drought in Northeast China

Global climate change exacerbates drought-induced agricultural and ecological degradation. Understanding complex meteorological–agricultural drought feedback is crucial for the development of effective mitigation strategies and sustainable agriculture. In this study, meteorological drought is quantified using the Standardized Precipitation Index (SPI), while agricultural drought is assessed via the Standardized Soil Moisture Index (SSI). By combining the Copula function, with the Random Forest method and SHAP value theory, this research provides a comprehensive analysis of drought interconnections, assessing both meteorological and agricultural drought dynamics and their influencing factors in Northeast China for the 2000–2023 period. The research results indicate that the following: (1) The monthly scale correlation between the SPI and the SSI in Northeast China shows significant seasonal differences. Summer has the strongest positive correlation, whereas winter has the weakest association. During the summer months, the highest positive correlation rate is 92.18 % in August, with that in July reaching a peak PCC value of 0.872. (2) The meteorological–agricultural drought reciprocal feedback relationship in Northeast China shows significant spatiotemporal differences. Spatially, positive feedback dominates in the southwestern part of the Songnen Plain, whereas negative feedback prevails in the Liaodong hills region. Temporally, the proportion within the reciprocal feedback threshold range of [0.5–2] reaches its peak in August at 37.11 %, indicating the establishment of a stable positive feedback mechanism during this month. (3) Low precipitation, high temperature, low soil moisture and high ET p significantly strengthen the negative feedback effect of agricultural drought on meteorological drought, with the synergistic effect of low precipitation and high temperature being the most prominent. These findings elucidate spatiotemporal drought interactions, surpassing static correlation limitations while establishing methodological references for cross-regional drought feedback analysis. • Establishing a Random Forest-SHAP framework quantifies interactions among factors. • Applied multivariate copula quantifies asymmetric drought feedback dependencies. • Positive feedback from agricultural to meteorological drought is the primary mode. • Clayton Copula is the best model for analyzing reciprocal feedback between SPI–SSI. • Low precipitation enhances negative feedback in meteorological–agricultural drought.