Assessing ecosystem risk is crucial to ecosystem management and to achieving the Paris Agreement’s 1.5 °C or 2.0 °C global warming targets, but the spatial pattern and drivers of ecosystem risk are highly controversial. By simulating China’s ecosystem water use efficiency using the Lund–Potsdam–Jena global vegetation model, the ecosystem risk at the eco-geographical and vegetation-type scales was assessed in this study to identify the regions at risk under global warming, and the impacts of the species richness (ASR) and micro-meteorological factors on the ecosystem risk were investigated in the identified regions using the structural equation model (SEM). Our results indicate that about 30.5% and 32.2% of the ecosystems in China will face risk under global warming targets of 1.5 °C and 2.0 °C, respectively. The ecosystem risk is spatially heterogeneous and biome-specific. The ecosystem risk will mainly occur in the mid-temperate humid/sub-humid region, warm temperate humid/sub-humid region, northern subtropical humid region, mid-subtropical humid region, and the Tibetan Plateau region in China, of which cropland, grassland, and deciduous broadleaf forests will account for 39%, 17%, and 14%, respectively. The results of the SEM analysis indicate that the micro-meteorological factors and ASR explain 48% of the variation in the ecosystem risk, and the micro-meteorological factors and ASR have similar but opposite influences on the ecosystem risk. For the ecosystems identified as at risk under global warming, the negative total effect of the micro-meteorological factors on the ecosystem risk is mainly due to the dominant role of the shortwave radiation in regulating the ecosystem risk; and the positive total effect of the ASR on the ecosystem risk indicates that the ecosystems with higher species richness may face more severe damage when the risk occurs. Therefore, we should carefully balance the effect of the ASR on the ecosystems’ abilities to adapt to changes and ecosystems’ abilities to recover from damage. Our results contribute to precise ecosystem management in China and provide evidence to support the idea that properly optimizing biodiversity can prevent and reduce the damage to ecosystems caused by global warming.
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