Climate change is expected to increase the risk of rainfall-induced landslides by altering rainfall characteristics. In particular, climate change can introduce rainfall nonstationarity, for example, through increases in rainfall intensity and frequency, and may also modify the dependence structure among rainfall variables. Accurately evaluating rainfall-induced landslide probability remains challenging because climate-driven rainfall changes are both time-varying and multivariate, whereas rainfall’s non-stationarity and dependence structure are still rarely considered in existing probabilistic landslide assessments. This study presents probabilistic frameworks for assessing rainfall-induced landslides in a changing climate by accounting for the nonstationarity and dependence structure (He et al., 2024, 2026a, 2026b). Using the total probability theorem, climate-driven rainfall hazards are integrated with slope fragilities to estimate landslide probability. Illustrative examples are provided for representative slopes in Hiroshima, Japan; Lishui, Southeast China; and Hong Kong. The results show that climate-driven rainfall nonstationarity can increase future landslide probability, while neglecting rainfall dependence can lead to considerable underestimation of landslide probability. These findings suggest that future landslide hazard assessment should move beyond single rainfall indicators and consider rainfall as a time-varying and mutually dependent process. This study provides new insights into the roles of rainfall’s nonstationarity and dependence structure in probabilistic landslide assessment under climate change.

Climate change,Landslides,Rainfall nonstationarity,Rainfall dependence structure,Probabilistic assessment