Abstract
Soil organic carbon (SOC) is an important component of the carbon pool in terrestrial ecosystems and plays a key role in climate feedbacks under global warming. To quantitatively assess the impact of climate warming on SOC in China, this study constructed six warming scenarios (0–3°C, 0.5°C step), and simulated the spatial and temporal distributions of SOC and changes in reserves by combining the RothC model and the Random Forest (RF) model. The results show that the model has high prediction accuracy in the mild warming scenario (scenarios with temperature increase of 0.5°C, 1°C, and 1.5°C), R 2 = 0.46–0.60, but the uncertainty increases in the high temperature scenario (scenarios with temperature increase of 2°C, 2.5°C, and 3°C), RMSE rises to 25.2 g C m -2 . With increasing temperature (the range of increase is 0-3°C), SOC in China showed significant spatial variability of "north increasing and south decreasing", and the overall reserves decreased by about 99.6 Tg on average (to be achieved roughly from the mid-to-late 21st century), showing a non-linear fluctuating downward trend. Dominant factors affecting SOC dynamics, with the vegetation factor having the highest explanatory power. The study reveals the mechanism by which climate factors and vegetation synergistically regulate SOC, which provides a quantitative basis for improving SOC modelling and understanding the process of the terrestrial carbon cycle under climate warming. • First construction of a multi-scenario warming system coupled RothC and Random Forest model. • A multi-scenario warming framework (0–3°C) quantified Soil organic carbon (SOC) responses across China. • Coupling RothC and Random Forest improved SOC prediction accuracy and robustness. • SHAP analysis identified vegetation as the dominant SOC driver. • The SOC showed a pattern of "increase in the north and decrease in the south" with a decrease of 99.6 Tg.