Abstract: With the intensification of global climate change, extreme rainfall events have become increasingly frequent, leading to recurrent rainfall-triggered landslides and causing significant casualties and economic losses. With the context of climate change, this study systematically reviews the research progress on advancements in probabilistic risk assessment of rainfall-triggered landslides, focusing on three key aspects: (1) slope reliability assessment under rainfall conditions considering climate change; (2) vulnerability assessment of slopes considering the uncertainty of rainfall patterns; and (3) rainfall-induced landslide hazard assessment based on machine learning methods. On this basis, this study further analyzes the multidimensional challenges faced by rainfall-triggered landslide risk assessment under climate change, including uncertainties associated with climate change, the lack of high spatio-temporal resolution geological and meteorological data, and the adaptability of models across different regions. Finally, from the perspectives of detailed geological surveys, multi-factor disaster gestation mechanisms, this study looks towards future research directions for enhancing resilience in rainfall-induced landslide disaster prevention, from landslide mechanisms under multiple factors, to resilience-based risk assessment. This study aims to provide theoretical support and methodological references for the disaster prevention and mitigation work of rainfall-triggered landslides, promoting the scientific, systematic, and refined development of landslide risk management.