###### Abstract  
Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems' responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive region, remains underexplored. This study aimed to develop a data-driven approach to predict the seasonal and annual variations in GPP in the Tibetan Plateau up to the year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed to investigate the relationships between GPP and various environmental factors, including climate variables, CO\({}_{2}\) concentrations, and terrain attributes. This study analyzed the projected seasonal and annual GPP from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four future scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The results suggest that the annual GPP is expected to significantly increase throughout the 21 st century under all future climate scenarios. By 2100, the annual GPP is projected to reach 1011.98 Tg C, 1032.67 Tg C, 1044.35 Tg C, and 1055.50 Tg C under the four scenarios, representing changes of 0.36%, 4.02%, 5.55%, and 5.67% relative to 2021. A seasonal analysis indicates that the GPP in spring and autumn shows more pronounced growth under the SSP3-7.0 and SSP5-8.5 scenarios due to the extended growing season. Furthermore, the study identified an elevation band between 3000 and 4500 m that is particularly sensitive to climate change in terms of the GPP response. Significant GPP increases would occur in the east of the Tibetan Plateau, including the Qilian Mountains and the upper reaches of the Yellow and Yangtze Rivers. These findings highlight the pivotal role of climate change in driving future GPP dynamics in this region. These insights not only bridge existing knowledge gaps regarding the impact of future climate change on the GPP of the Tibetan Plateau over the coming decades but also provide valuable guidance for the formulation of climate adaptation strategies aimed at ecological conservation and carbon management. Tibetan Plateau; gross primary productivity; climate change; spatiotemporal variation; convolutional neural networks 2020202024637233  
feedback [1; 4]. However, increasing evidence shows that the global CO\({}_{2}\) emissions continue to rise [5; 6]; this trajectory will lead to strong warming scenarios, exceeding 4 \({}^{\circ}\)C by the end of this century, with ongoing warming thereafter [7]. Therefore, gaining a comprehensive understanding of the impacts of climate change on the GPP of the Tibetan Plateau, and predicting its future variations under different climate scenarios, is essential in developing targeted emission reduction and carbon sequestration strategies. This knowledge will provide a robust scientific basis for the achievement of carbon neutrality goals. Over the past few decades, considerable studies have focused on assessing the impact of climate change on GPP. Common methods for the estimation of GPP typically involve process-based physical models that integrate physiological and ecological mechanisms related to vegetation growth, such as radiative transfer, photosynthesis, and respiration [8; 9]. These models are generally categorized as either land surface models (LSMs) or Earth system models (ESMs). LSMs are rooted in theories and hypotheses about ecosystem functioning and include various mechanistic or empirical sub-models to simulate fluctuations in ecosystem energy, water, and carbon fluxes [10; 11]. ESMs, on the other hand, consider physical, chemical, and biological processes within the Earth's ecosystems to model carbon cycle interactions between the biosphere and atmosphere [12; 13]. The strength of process-based physical models lies in their capacity to account for multiple processes and provide spatially continuous GPP estimates over long time series. For instance, [PERSON] et al.