Climate change is a major driver of vegetation activity but its complex ecological relationships impede research efforts. In this study, the spatial distribution and dynamic characteristics of climate change effects on vegetation activity in China from the 1980s to the 2010s and from 2021 to 2050 were investigated using a geographically weighted regression (GWR) model. The GWR model was based on combined datasets of satellite vegetation index, climate observation and projection, and future vegetation productivity simulation. Our results revealed that the significantly positive precipitation–vegetation relationship was and will be mostly distributed in North China. However, the regions with temperature‐dominated distribution of vegetation activity were and will be mainly located in South China. Due to the varying climate features and vegetation cover, the spatial correlation between vegetation activity and climate change may be altered. There will be different dominant climatic factors for vegetation activity distribution in some regions such as Northwest China, and even opposite correlations in Northeast China. Additionally, the response of vegetation activity to precipitation will move southward in the next three decades. In contrast, although the high warming rate will restrain the vegetation activity, precipitation variability could modify hydrothermal conditions for vegetation activity. This observation is exemplified in the projected future enhancement of vegetation activity in the Tibetan Plateau and weakened vegetation activity in East and Middle China. Furthermore, the vegetation in most parts of North China may adapt to an arid environment, whereas in many southern areas, vegetation will be repressed by water shortage in the future.

Central Asia (CA) is a continental region that is sensitive to water conditions. Hence, drought has one of the primary effects on the vegetation activities in CA and could vary with climate change. However, it is still unclear how the drought vulnerability of vegetation differs among vegetation types and varies with drought scales in CA. Therefore, this paper studied the drought vulnerability of vegetation in CA from 1982–2015. Droughts were detected by using the standardized precipitation evapotranspiration index (SPEI), and the vegetation activities were represented by the Normalized Difference Vegetation Index (NDVI). Only the areas with no change in vegetation types were analyzed, in order to avoid interference with changes in land use. Results showed that both the duration and intensity of droughts were higher in the central, southwestern, and northeastern CA. The growing season (April–October) NDVI decreased by −0.0095 ± 0.0065 per decade in response to drying trends of 0.21 ± 0.22 unit aridity index per decade in these drought-concentrated regions. Forests and savannas/woody savannas were more vulnerable to drought from July–September, and their vulnerabilities were higher to droughts with longer time scales. Shrublands and grasslands were more vulnerable to drought from April–May and May–September, respectively, and the vulnerabilities during these months were higher for the droughts at 6–12 months scales. Twelve months was the optimal (most vulnerable) drought scale for the shrublands and grasslands and the secondary drought scale for the savannas/woody savannas. Further analysis of the vulnerability of vegetation to 12 months drought found that it generally increased with the increase of the drought magnitude (duration or intensity) to some peak values and then decreased. However, the vulnerability of forests and savannas/woody savannas increased with the drought intensity. Results would help for the drought risk assessment of vegetation in CA.

Variations in forest net primary productivity (NPP) reflects the combined effects of key climate variables on ecosystem structure and function, especially on the carbon cycle. We performed risk analysis indicated by the magnitude of future negative anomalies in NPP in comparison with the natural interannual variability to investigate the impact of future climatic projections on forests in China. Results from the multi-model ensemble showed that climate change risk of decreases in forest NPP would be more significant in higher emission scenario in China. Under relatively low emission scenarios, the total area of risk was predicted to decline, while for RCP8.5, it was predicted to first decrease and then increase after the middle of 21st century. The rapid temperature increases predicted under the RCP8.5 scenario would be probably unfavorable for forest vegetation growth in the long term. High-level risk area was likely to increase except RCP2.6. The percentage area at high risk was predicted to increase from 5.39% (2021–2050) to 27.62% (2071–2099) under RCP8.5. Climate change risk to forests was mostly concentrated in southern subtropical and tropical regions, generally significant under high emission scenario of RCP8.5, which was mainly attributed to the intensified dryness in south China.