Abstract: In July 2024, Typhoon Gaemi triggered extreme rainfall in Zixing City, Hunan Province, China, causing numerous landslides. Accurate assessment of rainfall-induced clustered landslide hazards is important for regional disaster prevention, mitigation, and urban planning. Using this clustered landslide event as a case study, this study investigates the role of cumulative rainfall and short-term rainfall intensity in landslide hazard assessment. Based on a high-quality landslide inventory, rainfall data, and topographic and geological environmental factors, an assessment index system was established by incorporating cumulative rainfall and maximum hourly rainfall intensity. A random forest model was used for landslide hazard assessment, and feature-importance analysis and SHapley Additive exPlanations (SHAP) were used to quantify and interpret factor contributions. The random forest model showed good discriminative ability, with AUC values of 0.923 and 0.854 for the training and testing datasets, respectively. Landslide hazard in the study area exhibited significant spatial heterogeneity. Low and moderate hazard zones were mainly distributed around the periphery of the study area, whereas high and very high hazard zones were concentrated in the central part and accounted for 15.2% of the total area. Landslide density in the very high hazard zone reached 99.53 landslides/km2. Hazard distribution differed markedly among towns, with the western part of Bamianshan Yaozu Township identified as the most concentrated high-hazard area. Feature-importance and SHAP analyses showed that maximum hourly rainfall intensity and cumulative rainfall were the dominant controls on landslide occurrence, while topographic and geomorphological conditions provided the fundamental environmental setting. Cumulative rainfall and short-term rainfall intensity effectively characterize the triggering effects of extreme rainfall events on clustered landslides, and the random forest model can identify high-hazard landslide areas. The results provide a useful reference for landslide hazard identification and geohazard prevention under intense rainfall conditions.