Climate-driven extreme rainfall is increasing landslide risk, whereas conventional regional rainfall thresholds inadequately represent spatial variations in slope preconditioning across complex mountainous terrain. This study develops a dynamic early-warning framework that couples slope unit landslide susceptibility with rainfall triggering for Jinyang County, Southwest China. The county was divided into 7249 slope units, and eight conditioning factors were used to train support vector machine (SVM) and extreme gradient boosting (XGBoost) models with Bayesian hyperparameter optimization. Bayesian-optimized XGBoost (BO-XGBoost) achieved the best discrimination, with an area under the curve (AUC) of 0.893. For temporal triggering, 113 historical rainfall-landslide events were analyzed in logarithmic intensity–duration (I–D) space. A power-law fit described the central trend, and the fifth percentile of residuals defined a 5% non-exceedance threshold exceeded by approximately 95% of triggering events. Residual-based thresholds were used to establish four rainfall-trigger levels. A gated risk matrix coupled these levels with susceptibility classes. Validation using 162 online monitoring sites during the 21 August 2023 extreme rainfall event yielded 85.2% accuracy, 83.6% precision, 81.2% recall, 88.2% specificity, and an F1 score of 82.4%, demonstrating practical potential for spatially refined regional landslide early warning.