Abstract Identifying the relative importance of different socio-ecological drivers that affect the ecosystem services (ESs) clusters provides a potential opportunity for spatially targeted policy design. Taking Central Asia (CA) as a case study, the spatiotemporal distribution of seven ESs was evaluated at the state level, and then a principal component analysis and k-means clustering were applied to explore the ES clusters. Based on Spearman’s correlation coefficients, the trade-offs and synergies relationship between ESs were analyzed at the different ES clusters scales. A redundancy analysis (RDA) was used to determine the relative contribution of socio-ecological factors affecting the distribution of ES clusters. The ES quantification revealed the spatial consistency and separation among different types of ESs. Similarities and differences of the trade-offs and synergies among ESs existed in five ES clusters (i.e. ‘ESC1: agricultural cluster’, ‘ESC2: carbon cluster’, ‘ESC3: sand fixation cluster’, ‘ESC4: habitat cluster’ and ‘ESC5: Soil and water cluster’). Pairwise water yield, soil retention, carbon storage and net primary production had good synergetic relationships in ESC1, ESC2, ESC4 and ESC5; sand fixation displayed negative correlations with other ESs in all ESCs; and the trade-offs relationships existed between food production and habitat quality in ESC1, ESC2 and ESC5. The RDA demonstrated that the explanatory power of the ecological variables (e.g. climate and vegetation) to the spatial distribution of ES clusters was much higher than that of the socio-economic variables (e.g. population and GDP). An important information/recommendation provided by this study is that ES clusters should be treated as the basic ecological management unit in CA, and different management strategies should be designed in accordance to the major interactions among the ESs in each ES cluster.