Atmospheric drought, commonly represented by vapor pressure deficit (VPD), is increasingly recognized as an important constraint on vegetation phenology in water-limited regions. However, its role in shaping autumn phenology, particularly the end of the growing season (EOS), remains insufficiently characterized in complex multi-driver systems. Here, we developed a basin-scale diagnostic framework integrating Geographic Pattern Causality (GPC) and explainable machine learning (XGBoost-SHAP) to examine atmospheric drought constraints on EOS across the Yellow River Basin (YRB) during 1982-2022. Basin-wide EOS advanced significantly at a rate of -0.39 days yr(-1), accompanied by widespread increases in preseason VPD. GPC revealed spatially coherent directional coupling patterns between VPD and EOS under explicit assumptions, with stronger coupling in semi-arid grasslands and transitional vegetation zones than in humid forested regions. Explainable modeling identified divergent model-derived response descriptors across plant functional types, with grasslands showing an earlier internal response transition around 0.47 kPa, whereas forests exhibited a broader high-VPD response domain within the observed data range. In addition, a low-soil-moisture sensitivity regime centered near similar to 40 mm in the recent drier period was identified, within which EOS sensitivity to atmospheric demand intensified more rapidly. These results should be interpreted as observational diagnostic patterns and monitoring-oriented response descriptors, rather than independently validated causal mechanisms or universal ecohydrological thresholds. Overall, this study provides a transferable workflow for ecological monitoring in water-limited basins and generates testable hypotheses for future validation using field observations, manipulative experiments, and process-based models.