The spatial-temporal proximity effect in compound extreme climatic events refers to the interaction or cooccurrence (e.g., concurrent or sequential) of two or more extreme climatic events across spatial and temporal scales. The effect plays a critical role in shaping the types, regional variations, and spatiotemporal patterns of compound events. While concurrent compound events, without considering spatial-temporal proximity, have been widely investigated, other combinations and their associated impacts remain poorly understood. In this study, we propose a general framework of compound events considering spatial-temporal proximity effect. This framework includes four types: spatiotemporal overlap, spatial proximity, temporal proximity, and spatiotemporal proximity. We also present a methodology and develop open-access software for the classification, identification, and quantification of compound events. The proposed methodology is applied to identify hotspots and interannual variations in each type of compound heat-precipitation events in China based on the climate simulations from the Coupled Model Intercomparison Project Phase 6. The results reveal that the four compound event types exhibit similar spatial-temporal patterns, with high-frequency zones concentrated in the QinghaiTibetan Plateau and the middle-lower Yangtze River plains, averaging 26 occurrences annually. Furthermore, an amplification effect is observed in the identification of compound events, where temporal proximity exerts a stronger influence than spatial proximity. Specifically, compound events incorporating spatiotemporal proximity are characterized by the larger geographic extent, longer duration, and greater intensity, followed by those with temporal proximity, spatial proximity, and spatiotemporal overlap, respectively. Strong regional-scale variations in compound events are associated with the sequence of extreme events, event types, and preconditioning environmental factors. These findings highlight the importance of spatial-temporal proximity in modulating compound events, as variations in spatial-temporal scales can significantly influence related risks, such as those associated with temporal proximity.