Abstract. Developing an effective and reliable integrated drought index is crucial for tracking and identifying droughts. The study employs game theory to create a spatially variable weight drought index (GTDI) by combining two single-type indices: the agricultural drought index (SSMI), which implies drought hazard-bearing conditions, and the meteorological drought index (SPEI), which implies drought hazard-causing conditions. Also, the entropy theory-based drought index (ETDI) is induced to incorporate a spatial comparison to the GTDI to illustrate the rationality of gaming weight integration. Leaf Area Index (LAI) data is employed to confirm the reliability of the GTDI in identifying drought by comparing it with the SPEI, SSMI, and ETDI. Furthermore, an assessment is conducted on the temporal trajectories and spatial evolution of the GTDI-identified drought to discuss the GTDI’s advancedness in monitoring changes in hazard-causing and bearing impacts. The results showed that the GTDI has a greatly high correlation with single-type drought indices (SPEI and SSMI), and its gaming weight integration is more logical and trustworthy than the ETDI. As a result, it outperforms ETDI, SPEI, and SSMI in recognizing drought spatiotemporally, and is projected to replace single-type drought indices to provide a more accurate picture of actual drought. Additionally, GTDI exhibits the gaming feature, indicating a distinct benefit in monitoring changes in hazard-causing and bearing impacts. The case studies show drought events in the Wei River Basin are dominated by a lack of precipitation. The hazard-causing index SPEI dominates the early stages of a drought event, whereas the hazard-bearing index SSMI dominates the later stages. This study surely serves as a helpful reference for the development of integrated drought indices as well as regional drought mitigation, prevention, and monitoring.