<p>Landslide prediction is a core issue in disaster risk management. In rainfall-driven mountainous regions, landslide susceptibility evolves dynamically in response to changing hydro-meteorological conditions, yet most existing approaches remain static or retrospective and lack explicit temporal predictability. This study proposes a spatiotemporal modelling framework that formulates landslide susceptibility as a time-evolving system state driven by precipitation. The framework follows a two-stage workflow, in which dynamic environmental forcing is forecast prior to susceptibility estimation. A U-Net spatiotemporal model is first employed to predict short-term precipitation patterns, which are then integrated with static environmental attributes within an Extreme Gradient Boosting (XGBoost) susceptibility model. Model interpretability is enhanced using SHAP to quantify the relative contributions of dynamic and static predictors. The framework is demonstrated in Nepal using ten years of daily precipitation data to generate temporally explicit landslide susceptibility maps. The results show that the model achieves an accuracy of 90.54%. The proposed approach supports time-explicit landslide susceptibility prediction and provides a practical tool for hazard management and early-warning decision-making in rainfall-driven mountainous regions.</p>