Central Asia (CA), a region highly vulnerable to drought, is experiencing increasingly severe drought conditions under climate change. However, current understanding of the spatiotemporal dynamics of drought, its propagation mechanisms, and the quantitative contributions of key drivers, particularly snow cover and vegetation, remains limited. Therefore, a three-dimensional (3D) framework was employed to extract meteorological, hydrological, and agricultural drought (MD, HD, and AD) events and their propagation pairs, subsequently integrating the extreme gradient boosting model with the shapely additive explanations to systematically investigate the spatiotemporal evolution of growing-season drought events across CA, propagation characteristics from MD to HD (i.e., MD-HD) and MD to AD (i.e., MD-AD), and their dominant driving factors. The main findings are as follows: (1) MD, HD, and AD events exhibited similar spatiotemporal patterns, characterized by peak severity periods during 1940-1950 and 2010-2023, a southeast-northwest banded distribution of high-severity centroids, and a dominant east-west migration direction (>73 %), (2) analysis of MD-HD and MD-AD propagation revealed a spring-season concentration (>52 %), geographically distinct hotspots (i.e., MD-HD and MD-AD in the northwestern lowlands and the northern agricultural zones, respectively), and a prevalent east-west propagation direction (>67 %), and (3) MD severity exerted the strongest influence on propagation, with elevated LAI facilitating and increased snow depth/snowmelt suppressing this process, further compounded by synergistic effects among MD characteristics. This study provides a scientific basis for the early warning of drought-induced disaster chains in arid and semi-arid regions.