This study presents a methodological framework to investigate the impacts of climate change on rainfall-triggered landslides at the subregional scale. Focusing on a ∼170 km² area in the Partenio Mountains in southern Italy, we employed regional rainfall projections (CORDEX) under moderate (RCP4.5) and high (RCP8.5) emission scenarios for 2006–2070. Rainfall data were bias corrected with observations from 2006–2023 and benchmarked against a synthetic dataset generated through stochastic reproduction of currently observed conditions. Physically based simulations of hydrological processes, coupled with slope stability analyses that account for unsaturated soil conditions, enabled event-by-event identification of landslides throughout the period. Statistical comparisons between scenarios were conducted across three rainfall homogeneous subregions. Results show a general tendency toward drier soil conditions, consistent with regional-scale climate studies, but with increasing rainfall variability across subregions. Despite this drying trend, projections indicate a significant rise in landslide occurrence, with a faster increase under RCP4.5 when compared to RCP8.5. This counterintuitive outcome reflects shifts in rainfall dynamics: under RCP8.5, landslides are mainly linked to more intense triggering rainfall, while under RCP4.5 they result from a combination of wetter antecedent conditions and more intense early-peak rainfall events. These findings emphasize the critical role of antecedent soil moisture in landslide initiation by showing its stable influence on landslide occurrence despite the rapid evolution of climate change. Overall, the methodology provides a transferable framework to assess local climate change impacts on geohazards by integrating bias-corrected climate projections with physically based hydrological–geomechanical modeling.

The vulnerability to landslides depends on both the susceptibility of the exposed elements and the intensity of the landslide, which is commonly characterized by its motion mechanism. This study proposes a quantitative evaluation framework to assess the implications of using different models for predicting the landslide runout distance (LRD) on vulnerability, estimated through two distinct vulnerability functions. The analysis focuses on a debris flow that impacted a major highway in the Colombian Andes. The event, with a triggered volume of 340 m³, a runout of 84 m, and a vertical drop of 42 m, serves as a benchmark for evaluating model performance. The findings provide insights into the influence of material type, flow regime, and model uncertainty on LRD and vulnerability estimates. Empirical methods enabled rapid assessments but exhibited high variability (LRD errors up to 120 %). Analytical models, particularly the sliding block model, offered a balance between simplicity and physical realism, overestimating LRD by 14 % without calibration while also providing velocity estimates. Multidimensional (2D/3D) models, though resource-intensive, best reproduced the observed behavior; the 3D model closely matched the measured runout when calibrated with high-friction parameters and GIS-derived inputs. A benchmarking analysis using the Analytic Hierarchy Process (AHP) identified the sliding block model as the most effective overall, combining accuracy, functionality, and usability. These results highlight that model selection should align with the intended application: empirical models for rapid screening, analytical models for design purposes, and multidimensional models for detailed vulnerability assessments.