Soil erosion poses a significant threat to land conservation, freshwater security, and ocean ecology. Climate change, with rainfall as one of its primary drivers, exacerbates this problem. Therefore, reliably predicting future soil erosion rates and taking into account anthropogenic influences are crucial for policymakers and researchers in the earth-system field. To address this challenge, we have developed a novel framework that combines the Bayesian Model Averaging (BMA) method with the Revised Universal Soil Loss Equation (RUSLE) model to estimate erosion rates on a national scale. We used BMA to merge five Regional Climate Models (RCMs), reducing uncertainty in ensemble simulations and improving the plausibility of projected changes in climatic regimes over China under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). The RUSLE model was applied to forecast the effects of climate change and land-use change on water erosion in China, using high-resolution climate simulation and prediction inputs. Our findings revealed that under the RCP4.5 and RCP8.5 scenarios, average annual soil loss will increase by 21.20% and 33.06%, respectively, compared to the baseline period. Our analysis also demonstrated a clear distinction between the effects of climate change and land-use change on water erosion. Climate change leads to an increase in precipitation, which exacerbates water erosion rates, with contributions ranging from 59.99% to 78.21%. Furthermore, an increase in radiative forcing will further amplify the effects of climate change. The transformation of land from one that has not been disturbed by humans to one that has been exposed to some soil and water conservation measures will have a mitigating effect on water erosion, with a contribution of −6.96% to −4.68%. Therefore, implementing effective soil and water conservation measures can somewhat mitigate the severity of ongoing soil loss. Our findings have significant implications for policymakers seeking to develop national strategies for soil conservation and model developers working to reduce uncertainty in erosion predictions.

Anthropogenically induced global warming intensifies the water cycle around the world. As a critical sector of the water cycle, snow depth and its related extremes greatly impact agriculture, animal husbandry, and food security, yet lack investigation. In this study, five high-resolution climate models are selected to simulate and project snow depth and its extremes over China. The simulation capabilities of models in reproducing the basic climate variables in winter are gauged in terms of spatial and temporal patterns over nine subregions. It is found that the driving global climate model (GCM) can contribute to similar patterns, while the different regional climate model (RCM) schemes lead to large variations in the snowfall accumulating on the land surface. The warming magnitude is larger under a higher representative concentration pathway (RCP) scenario (2.5 °C greater under RCP8.5 than RCP4.5). The distribution of ensemble mean winter precipitation changes is more fragmented because of the relatively low skill in reproducing water-related content in the climate system. The projected precipitation change is larger under RCP8.5 than under RCP4.5 due to the amplification of the hydrological cycle by temperature warming. The projected changes in the ensemble mean snow depth mainly occur over the Tibetan Plateau with a decreasing trend. Only several grids over the Himalayas Mountains and the upper stream of the Yarlung Zangbo River are projected with a slight increase in snow depth. Both the intensity and frequency of extreme snow events are projected to increase in Northeast China and Inner Mongolia, which are important agricultural and animal husbandry production areas in China. The reason behind this projection can be explained by the fact that the hydrological cycle intensified by temperature warming leads to excessive snowfall stacking up during winter. The changes in extreme snowfall events in the future will have a significant impact on China’s agricultural and animal husbandry production and threaten food security.