###### Abstract  
To investigate the spatiotemporal evolution characteristics and primary driving factors of Gross Primary Productivity (GPP) on the Qinghai-Tibet Plateau, we employed an enhanced MODIS-PSN model. Utilizing the fifth-generation global climate reanalysis dataset (ECMWF ERA5), we generated GPP remote sensing products by integrating six natural factors. Through correlation analysis and geographical detector modeling, we quantitatively analyzed the spatiotemporal dynamics and key drivers of vegetation GPP across the Qinghai-Tibet Plateau from 2001 to 2022. The results demonstrate that GPP changes across the Qinghai-Tibet Plateau display pronounced spatial heterogeneity. The humid northeastern and southeastern regions exhibit significantly positive change rates, primarily distributed across wetland and forest ecosystems, with a maximum mean annual change rate of 12.40 gC/m\({}^{2}\)/year. In contrast, the central and southern regions display a decreasing trend, with the minimum change rate reaching \(-1.61\) gC/m\({}^{2}\)/year, predominantly concentrated in alpine grasslands and desert areas. Vegetation GPP on the Qinghai-Tibet Plateau shows significant correlations with temperature, vapor pressure deficit (VPD), evapotranspiration (ET), leaf area index (LAI), precipitation, and radiation. Among the factors analyzed, LAI demonstrates the strongest explanatory power for spatial variations in vegetation GPP across the Qinghai-Tibet Plateau. The dominant factors influencing vegetation GPP on the Qinghai-Tibet Plateau are LAI, ET, and precipitation. The pairwise interactions between these factors exhibit linear enhancement effects, demonstrating synergistic multifactor interactions. This study systematically analyzed the response mechanisms and variations of vegetation GPP to multiple driving factors across the Qinghai-Tibet Plateau from a spatial heterogeneity perspective. The findings provide both a critical theoretical framework and practical insights for better understanding ecosystem response dynamics and drought conditions on the plateau. 2025 16 940 10.3990 10. GPP is essential for human survival and development, serving as the foundation of the global carbon cycle and playing a key role in maintaining greenhouse gas concentrations and regulating the global climate. The Qinghai-Tibet Plateau has been called the \"water tower of Asia\" [5], and its ecosystems are more fragile and sensitive to climate change and anthropogenic disturbances at higher altitudes than at lower altitudes [6; 7]. Over the past 20 years, human activity on the Qinghai-Tibet Plateau has intensified more than the global average, with the region's warming rate being twice that of the global land average [8; 9]. In the context of climate change and increasing human disturbances, investigating the variations in vegetation GPP and its driving factors on the Qinghai-Tibet Plateau is crucial for assessing the region's ecosystem health and formulating effective strategies to preserve its ecological stability [10]. Studies on multiple environmental factors primarily use methods such as general linear modeling, principal component analysis (PCA), structural equation modeling (SEM), geographical detector (GD) modeling, and covariance analysis [11; 12; 13]. Factor detectors are used to assess the explanatory power of individual factors on a variable [13]. This enables one to analyze data more objectively, quantify indicator factors, and identify their coupling with geography. The q-value (q-statistic value) is used to characterize decision power, with higher q-values indicating stronger explanatory power. The GD model is a widely used statistical tool for geographical attribution analysis, offering a new spatial statistical method to detect spatial heterogeneity and identify determinants related to risk, factors, ecology, and interactions. It includes various detectors such as factor, ecological, risk, and interaction detectors. The aim of this study was to identify the key drivers affecting GPP and their interaction mechanisms.