## 1 Introduction  
Human-induced climate change beyond natural climate variability has caused widespread adverse impacts and damage to ecosystems [1, 2]. Global warming, which is expected to reach 1.5 \({}^{\circ}\)C shortly (currently 1.2 \({}^{\circ}\)C) [2], presents multiple risks to ecosystems that will result in irreversible impacts on polar and mountain ecosystems with low resilience [2]. Previous studies have indicated that high-altitude and high-latitude regions are more sensitive to climate change than other regions [3, 4, 5]. The Qinghai-Tibet Plateau (QTP) is the highest and one of the most climatically sensitive and ecologically fragile regions in the world [6, 7, 8]. The QTP exerts a profound impact on both the regional and global climate [9, 10, 11]. Moreover, its influence extends beyond Asia, affecting environmental changes throughout the northern hemisphere [9, 12]. With a warming rate of \(\sim\)0.4 \({}^{\circ}\)C decade\({}^{-1}\) (almost twice the global mean) and a wetting trend [9, 13], the plateau has experienced great eco-environmental changes over the last 50 years [5, 14]. With its coverage spanning over 60% of the QTP, alpine grassland constitutes the dominant vegetation type in the region [15, 16, 17]. It accounts for 44% of China's grasslands and 6% of the world's grasslands [7, 18]. The structure and function of the alpine grassland have changed dramatically since the 1980s due to climate change [19, 20, 21]. Warming and wetting have greatly enhanced the productivity and respiration of the grassland ecosystem on the plateau [22; 23]. However, in the context of carbon neutrality, greater attention is being paid to the spatiotemporal variation of net carbon uptake, which has not yet been systematically evaluated. In addition, the respiratory sensitivity of the ecosystem on the plateau will decrease significantly as the climate warms [24], but the extent of the decrease is unclear. Therefore, it is necessary to systematically evaluate the carbon budget and its climatic driving mechanism in the alpine grassland on the QTP (AGQTP). Process-based ecosystem models have become an important method for the carbon budget assessment of global and regional ecosystems [25], including the Global Carbon Project [26]. However, there are often large gaps in the carbon budget results estimated using different models [25; 27]. Estimating regional carbon budgets in the QTP is challenging due to the complexity of vegetation, soils, and weather patterns, and a lack of adequate ground-based data for model calibration and validation [20; 23]. Additionally, climate change is impacting temperature and precipitation patterns at an accelerated rate, further complicating the estimation of regional carbon budgets [20; 28]. Research based on the ORCHIDEE (Organizing Carbon and Hydrology In Dynamic Ecosystems) model showed that the carbon sink of the AGQTP in the 2000s was 21.8 Tg C yr\({}^{-1}\)[7], while research based on the Terrestrial Ecosystem Model showed that the carbon sink was about 16.5 Tg C yr\({}^{-1}\) during the same period [29]. However, a recent study utilizing the CASA (Carnegie-Ames-Stanford Approach) model and the soil heterotrophic respiration model developed by [PERSON] [30] indicated that the carbon sink during the period of 2001-2015 was approximately 128 Tg C yr\({}^{-1}\), significantly surpassing the previous findings of the aforementioned research [31]. There is also great controversy about the driving mechanism of the carbon budget of the AGQTP. A previous study revealed that variations in CO\({}_{2}\) concentration and precipitation were the main drivers of changes in the carbon sink of the AGQTP during 1961-2009, and although climate warming benefited vegetation growth, it did not significantly accelerate the net carbon uptake due to its acceleration of soil organic matter decomposition [7]. A study based on field experiments and meta-analysis has also shown that under drought conditions, the indirect effect of warming will inhibit the direct effect of temperature increases on the NEP [32].