Landslides pose a significant threat to human lives and property. Evaluating dynamic changes in landslide susceptibility under climate change can provide decision-making support for future disaster prevention. Using historical landslide inventories (2008–2023) and a random forest algorithm, this study develops an annual-scale landslide susceptibility model to assess spatiotemporal patterns of landslide susceptibility across China under different simulated scenarios. The results show that model achieves excellent performance (AUC = 0.97), with annual precipitation being the most influential factor (26 % contribution). Compared to the baseline (1950–2014), China's landslide susceptibility is projected to increase significantly under future climate conditions. By the late 21st century (2076–2100), the national mean annual precipitation is expected to rise by 59–111 mm, corresponding to a 4.3–10.6 % expansion in median to very high susceptibility zones across SSP scenarios. Spatially, the most significant susceptibility increases are anticipated in the Northwest Loess Plateau region (Loess) near the Taihang Mountains and the northern part of the Southwest Karst Mountain region (SW), where SSP5-8.5 amplify risks toward the century’s end. These findings underscore the necessity of proactive risk management in these identified hotspots to mitigate escalating landslide threats.