Causal Inference of Cascading Effects and Their Driving Factors Among Multiple Drought Types in the Yangtze River Basin

ABSTRACT While accurate assessment of drought evolution is a prerequisite for effective early warning and mitigation, conventional methodologies often struggle to characterise the intricate propagation pathways and feedback mechanisms inherent to the Yangtze River Basin (YRB). To address these deficiencies, this study establishes a causal inference framework for drought cascades by integrating convergent cross mapping (CCM), propensity score matching (PSM) and logistic regression. This approach facilitates the quantitative disentanglement of causal interactions among meteorological (MD), agricultural (AD) and hydrological droughts (HD), while effectively isolating confounding environmental variables. Our findings revealed that, in addition to the traditional MD–AD–HD propagation sequence, typically characterised by a 1‐month lag, there existed significant, previously overlooked feedback loops (AD–MD, HD–MD and HD–AD) lacking strong lag signals, with a 0‐month lag accounting for 63% of the YRB. Notably, the transformation risk from AD to MD was the highest (60%), whereas that from MD to HD was the lowest (20%), indicating that drought feedback mechanisms exert a consistently stronger influence than conventional downward propagation. Furthermore, precipitation (Pre) and vapour pressure deficit (VPD) were identified as the primary mitigating and intensifying drivers of these cascades (both propagation and feedback), respectively, with a unit increase in Pre reducing the drought cascade risk to as low as 0.18 times and a unit increase in VPD amplifying it to more than 3.0 times. The intensity of these interactions is further amplified in regions with lower altitudes and high soil clay content. Overall, this research offers a novel perspective on the bidirectional coupling of hydro‐meteorological extremes, providing a robust scientific framework for nuanced drought risk management.