###### Abstract
[10] evaluated the performance of the second version of the Beijing Climate Center Atmosphere-Vegetation Interaction Model in simulating the GPP across various spatial and temporal scales. However, these models also have limitations. The inclusion of numerous physical processes introduces uncertainties in the model parameters and reduces the adaptability to large-scale regions. For example, [PERSON] et al. [14] assessed the GPP simulation results of seven general circulation models (GCMs) and found that no single climate model accurately captured the interannual variation in GPP and ecosystem responses to climate change, particularly in the Tibetan Plateau. Consequently, there is significant uncertainty in future GPP predictions when relying on process-based models [15]. Another powerful approach for the evaluation of the impact of climate change on GPP involves the use of machine learning (ML) models. With advancements in remote sensing and ML technologies, the assessment and estimation of GPP using ML models have become a prominent research focus [16; 17; 18]. Leveraging their strong non-linear fitting capabilities, ML models are adept in capturing the complex non-linear relationships between GPP and various influencing factors, such as meteorological and soil conditions. For instance, [PERSON] et al. [19] employed a model tree ensemble algorithm to create a GPP dataset over China with a spatial resolution of 0.1\({}^{\circ}\), spanning from 1982 to 2015, using in situ data from 40 flux sites. To further improve the precision of GPP simulations, researchers have also conducted comparative studies on the performance of different ML models [20; 21]. For example, [PERSON] et al. [22] compared six ML models--a multilayer perceptron, random forest [23], Adaboost, gradient boosting decision tree, XGBoost, and LightGBM--trained using remote sensing data and various influencing factors. In addition to their accuracy in GPP simulation, ML models contribute to the attribution analysis of GPP variations. For instance, [PERSON] et al. [19] identified the temperature as the dominant climatic driver of the GPP at a national scale. Similarly, [PERSON] et al. [24] showed that the leaf area index and temperature-related factors had the largest relative impacts on the GPP in Northern and Southern China, respectively. This capability is crucial in evaluating the impacts of climate change on GPP. Unlike process-based physical models, which rely on a set of fixed input variables, ML-based models can incorporate a broader range of variables--such as greenness and the land surface temperature--to enhance the GPP simulation accuracy [25]. Despite the substantial advantages of ML models in predicting GPP, research in this field, particularly in the Tibetan Plateau, remains limited. Most current GPP prediction efforts continue to rely on process-based physical models. For instance, [PERSON] et al. [26] employed the CoLM model to examine the effects of changes in land use and land cover on the future terrestrial GPP. Similarly, GCMs have provided valuable insights into GPPdynamics. [PERSON] et al. [27] highlighted the potential risks to GPP in the high-latitude regions of the Northern Hemisphere due to climate change, which poses a direct threat to the global land carbon sink. Furthermore, [PERSON] et al. [28], drawing on data from 13 ESMs, underscored the increasing influence of extreme droughs on GPP throughout the twenty-first century. Projections by [PERSON] et al. [29] indicate divergent trends in GPP, with expected increases in the Northern Hemisphere and decreases in tropical regions. The existing studies on ML used to predict GPP are concentrated on global-scale projections. For example, [PERSON] et al. [30] analyzed future global GPP trends using datasets from 18 ESMs in CMIP6, showing an overall increase in GPP across all four SSP scenarios, with the ssp585 scenario stimulating the most substantial growth. Moreover, the study of [PERSON] et al.