## 3 Results
### Model-Based NDVI Future Trend Prediction  
To thoroughly examine the impact of future climate change on vegetation health in China, this study adopted multiple emission scenarios to evaluate the trends in NDVI changes. Based on this approach, this study plotted the spatial distribution of the model's RMSE across China, as shown in Figure S7, which shows the obtained predictions of NDVI and its spatial distribution for China under four different SSP scenarios (namely SSP126, SSP245, SSP370, and SSP585) (Figure 5). The results indicate that the spatial distribution of NDVI values exhibits distinct regional characteristics across all SSP scenarios. Under all four scenarios, China's NDVI generally presents a pattern of being lower in the northwest and higher in the southeast, closely related to regional precipitation patterns and climatic conditions. Water limitations in arid and semi-arid regions result in lower NDVI values in northern and northwestern areas such as Inner Mongolia, Gansu, and Xinjiang. Specifically, long-term drought, limited rainfall, and high evaporation rates in these regions adversely affect vegetation growth, leading to low NDVI figures. In contrast, the southeastern humid zone maintains higher NDVI levels due to abundant precipitation and suitable climatic conditions. Under the SSP126 scenario, although some high-value areas exist, they are predominantly concentrated in South China, yet not significantly. Comparatively, under the SSP245 scenario, high-value areas concentrate in Jiangxi Province, the Guangxi Zhuang Autonomous Region, Guangdong Province, and eastern Hunan Province. Under the SSP370 scenario, regions with high NDVI values are more extensive, focusing on Jiangxi, Hunan, and several provinces along the southeastern coast, with the largest range of high-value areas among all scenarios, potentially reflecting increased precipitation and temperature rise promoting vegetation growth. Under the SSP585 scenario, higher NDVI values occur in eastern Guizhou, western Hunan, and southern Chongqing, possibly associated with climate change patterns in this region under strong greenhouse gas emissions, particularly improvements in water supply and temperature suitability. Notably, under the SSP370 and SSP585 scenarios, NDVI values in Northeast China are markedly higher than in other scenarios, indicating that climate changes in these high-emission scenarios may enhance vegetation growth in this region. Under varying SSP scenarios, the average NDVI nationwide is 0.3072 under the lower emission scenario SSP126; this value increases progressively in higher emission scenarios to 0.3218 under SSP245 and 0.3419 under SSP370, and it reaches 0.3678 under the highest emission scenario, SSP585. This indicates that, although increased greenhouse gas emissions theoretically lead to global warming and its associated negative impacts, from the perspective of vegetation health, vegetation conditions are projected to improve with rising emissions. However, under the high-emission scenario SSP370, the distribution of NDVI exhibits a polarized trend: the maximum value significantly rises to 0.7636, while the minimum drops to 0.04845. This suggests that while certain suitable areas may maintain or increase their vegetation cover, arid, semi-arid regions, or areas with poor environmental conditions might experience more severe vegetation degradation amid frequent extreme climatic events and intensified resource competition. Notably, although the spatial average NDVI peaks under the highest emission scenario, SSP585, its maximum value is slightly lower than under the SSP370 scenario (0.7605), indicating that vegetation responses are influenced not only by CO\({}_{2}\) concentration but also by other environmental stresses and resource availability resulting from climate change. The impact of future climate change on vegetation will be multidimensional and complex, necessitating a comprehensive consideration of multiple factors to fully understand the trends in vegetation dynamics.