The interaction between snow depth (SD) and land surface temperature (LST) is a critical yet underexplored process in arid mountain hydrology. This study introduces an integrated analytical framework combining Coupling Coordination Degree Model (CCDM) and time-lagged correlation analysis to systematically quantify the interaction strength, synergistic quality, and dynamic response times between SD and LST across the complex mountain-basin systems of Xinjiang, China. Using long-term, high-resolution remote sensing data, we reveal a hierarchical control system governing snow–temperature interactions: macro-scale latitudinal climate divides establish a north–south contrast in coupling potential; meso-scale topography overrides this pattern in southern mountains, where elevation becomes the dominant control on coupling and coordination; and micro-scale local factors drive east–west divergences in response lags. Key findings include: (1) pronounced north–south asymmetry in the Tianshan Mountains, with the sensitive south slope showing significant spring lag lengthening; (2) elevation-dependent thresholds in the Kunlun Mountains, where snow–temperature coordination improves only above 3500 m; and (3) region-specific lag dynamics indicating altered snowpack thermal inertia (e.g., prolonged spring lags in the Tianshan) and memory effects. The discrepancy between coupling degree and coordination degree emerges as a key diagnostic, identifying vulnerable regions where strong temperature forcing is mismatched with sustainable snowpack evolution. This study provides a process-aware framework that moves beyond statistical correlation, offering quantitative metrics to improve the representation of mountain snow–climate feedbacks in hydrological and climate models under accelerating warming.