## 1 Introduction
(2) The second approach is regression models [29; 30; 31], which are built on the basis of statistical analysis to identify relationships between susceptibility and influential factors. While very simple in their implementation and quantitatively accurate, they fail to represent complicated nonlinear relationships. (3) The third approach is machine learning models, which require large datasets and the proper selection of factors, ratios, and algorithms [32]. Common models include support vector machines (SVMs) [33], random forests (RFs) [34], neural networks [35], Bayesian networks [36], k-nearest neighbors (KNNs) [37], and XGBoost (XGB) [38; 39]. All these models are able to handle nonlinear relations and large datasets fairly well, and they demonstrate excellent generalizability if they are well fitted. A landslide susceptibility assessment by means of machine learning models involves extracting relevant factors from many domains, such as geology, topography, hydrology,vegetation, soil, and human activities [40]. Precipitation is the primary natural trigger of landslides, as it increases soil moisture, softens the soil, reduces shear strength, and causes slope instability. Landslides often have a close relationship with extreme rainfall or prolonged precipitation events [41]. While climate change enhances the uncertainty of extreme precipitation events, it also increases the probability and magnitude of landslides [42]. Although the importance of precipitation in landslide prediction is widely recognized, most previous machine learning studies either focused on annual precipitation, which has a low correlation with landslide occurrence [43], or used daily precipitation data to explore the relationship between landslides and precipitation [44]. In fact, landslide occurrence is often closely linked to extreme rainfall or prolonged rainfall events, meaning that effective antecedent precipitation (EAP) has a strong correlation with landslide occurrence compared to other precipitation indices. Considering EAP during landslide susceptibility assessments is crucial for improving the accuracy of landslide prediction. By capturing the cumulative effects of precipitation, EAP enhances the accuracy and reliability of landslide predictions. Considering precipitation intensity and duration, along with their spatial correlation with landslides, will lead to more precise prediction models. Compared to other factors, the variability of EAP is substantial, adding significant uncertainty to landslide susceptibility assessments. Thus, it is essential to account for landslide susceptibility under various antecedent precipitation probability scenarios. This study proposes an innovative approach to landslide susceptibility assessments by integrating cumulative rainfall effects through the effective antecedent precipitation (EAP). Specifically, we conducted the following:  
* Based on EAP data from 2064 historical landslides, a high-precision landslide prediction model was developed. Compared to coarse-grained precipitation metrics, the EAP data more accurately capture the mechanism of rainfall-triggered landslides, significantly improving the model's predictive accuracy. Additionally, the analyses of both the mean decreases in the Gini and SHAP values indicate that EAP is a key factor contributing to landslides. * Using daily precipitation records from 69 meteorological stations, we evaluate eight probability distribution models to characterize EAP across various return periods, thereby quantifying multiple antecedent rainfall scenarios for hazard analysis. * We generate dynamic landslide susceptibility maps under different rainfall return periods (5, 10, 20, and 50 years), which identify high-risk zones and illustrate how extreme precipitation probability scenarios impact landslide spatial patterns. These contributions demonstrate a more accurate and scenario-inclusive framework for rain-induced landslide prediction, providing valuable insights for early warning and risk management in mountainous regions.