Introduction: Amid the escalating challenges of global climate change, the stress effects of compound extreme climate events on terrestrial ecosystems are becoming increasingly prominent. However, a critical knowledge gap persists in quantitatively dissecting their synergistic impact on vegetation gross primary productivity (GPP). Methods: This study introduces a novel framework that integrates spatiotemporal trend analysis, correlation methods, and the geographical detector model. This integrated approach is designed to quantify the non-linear interactions between compound hot extremes and drought events, and GPP across China, and to systematically reveal the interaction mechanisms among their characterizing indicators—namely, the Standardized Temperature Index (STI), the Standardized Precipitation Evapotranspiration Index (SPEI), and GPP. Results: The results show that: (1) From 2001 to 2020, the interannual variation of GPP in China's six major regions showed an increasing trend, 98.84% have experienced significant warming, and 83.76% show no significant changes in drought. (2) The occurrence of compound hot extremes and drought events shows significant regional heterogeneity, with a frequency of 14.4% in North China and 13.1% in Northwest China. Compound hot extremes and drought events exhibit distinct regional heterogeneity, being most frequent in North China and least frequent in Northwest China. (3) Correlation analysis indicates that 72.4% of regions show a negative correlation between STI and GPP; 64.1% show a positive correlation between SPEI and GPP; and 71.69% show a negative correlation between the frequency of compound hot extremes and drought events and GPP. (4) Interaction effect analysis highlights that the impact of compound hot extremes and drought events on GPP exceeds that of either factor alone, with the most significant effect in North China (q values of STI, SPEI, and compound hot extremes and drought events are 0.14, 0.25, and 0.51, respectively. Moreover, the interaction exhibits a synergistic amplification. Discussion: This study provides new data support for assessing ecosystem resilience and informing adaptive management under climate change.

Net primary productivity (NPP) is a vital indicator of carbon sequestration and ecosystem resilience. However, the dynamics of NPP across different land use types and especially the interactive function of natural drivers remain insufficiently quantified in regions with significant land use change. Therefore, this study selected Heilongjiang Province in China as the research area. Utilizing multi-source data from 2001 to 2022, it identified the primary land use types, analyzed the mean values and trends of vegetation NPP for each type, and quantified the driving effects of natural factors on NPP across these land types. Results show that forests had the highest mean NPP (514.01 gC m⁻²·a⁻¹) and shrub–grass–wetland composites the lowest (269.2 gC m⁻²·a⁻¹); cropland-to-forest transitions boosted NPP most notably. Critically, precipitation–temperature interactions dominated NPP variation, while elevation acted mainly through modulating other factors. This study offers a strategic framework for spatial planning and ecosystem management, supporting climate mitigation and carbon sequestration policies.