###### Abstract
The main drivers selected were LAI, temperature, VPD, ET, precipitation, and radiation. This study focused on the assessment of spatial heterogeneity, identification of influencing factors, and in-depth analysis of interactions between variables. Over the past 40 years, the spatial and temporal patterns of vegetation GPP on the Qinghai-Tibet Plateau have been characterized by a number of studies showing that the vegetation GPP on the Qinghai-Tibet Plateau is increasing, with \"overall increase and local decrease\" [14]. [PERSON] et al. [15] investigated the spatial pattern of interannual changes in GPP on the Qinghai-Tibet Plateau from 2001 to 2019, which was \"low in the northwest and high in the southeast\" using a linear trend detection method. Vegetation growth is influenced by a combination of various factors. These factors are typically categorized into two groups: natural factors and human activities. Studies indicate that the primary drivers of robust vegetation growth in the Northern Hemisphere are the increase in global atmospheric carbon dioxide concentrations and rising temperatures due to global warming [16]. Numerous previous studies have shown that climatic factors such as temperature and precipitation are the key drivers influencing the growth and development of vegetation [17]. ET is one of the important variables in the terrestrial hydrological cycle, and evapotranspiration on the Qinghai-Tibet Plateau has shown complex trends under the influence of warming and humidification [18]. Precipitation also affects vegetation growth. Some studies have shown that the relationship between vegetation cover, temperature, and rainfall varies in stages and may even exhibit a negative correlation [19]. There is a need to explore the relationship between the interannual variability of GPP, climate factors, and different vegetation types on the Qinghai-Tibet Plateau [20]. Factors influencing vegetation change across the country based on the leaf area index [21]. The relationship between vegetation cover and GPP also varies by season, with the positive correlation being strongest in summer. In spring, summer, and autumn, the areas where GPP and NDVI are significantly correlated are mainly distributed in the southeastern Qinghai-Tibet Plateau [22]. Combined with previous studies, LAI, precipitation, temperature, radiation,VPD, and ET were selected as driving factors in this study to analyze the spatial variability of vegetation GPP in the Qinghai-Tibet Plateau. The study found that both climate factors and human activities play significant roles in vegetation cover changes, highlighting the strong correlation between climate change and vegetation dynamics. It also emphasized that human activities, such as policies like returning farmland to forests, have positively contributed to vegetation restoration [23]. The default parameters of the MODIS-PSN model show large deviation on the Qinghai-Tibet Plateau; using the MCMC parameter optimization method and measured data, the MODIS-PSN model was calibrated, which greatly improved its simulation accuracy, and, as a result, a set of GPP model parameters suitable for the Qinghai-Tibet Plateau was obtained. Using the reanalysis information and MODIS remote sensing data, the regional-scale GPP of the Qinghai-Tibet Plateau was estimated, and it was found that the GPP of the Qinghai-Tibet Plateau has shown a significant increasing trend in the last 20 years, and the relationship between the GPP of the Qinghai-Tibet Plateau and the driving factors (LAI, temperature, precipitation, radiation, VPD, and ET) was analyzed using correlation analysis and GD. Therefore, it is of great significance to study the changes in vegetation GPP and its relationship with driving factors in the Qinghai-Tibet Plateau region, to quantitatively investigate how changes in vegetation GPP relate to climatic factors, and to safeguard the ecological security of the Qinghai-Tibet Plateau while improving the ecological environment.