Abstract: The Rainfall thresholds are among the most commonly used criteria for predicting rainfall-induced landslides. However, existing empirical rainfall thresholds mainly focused on regional landslide warnings, lacking consideration for the spatial variability of rainfall thresholds for individual landslides within the region. This study uses historical rainfall-induced landslide data and hourly rainfall data from Bazhong City (2014 – 2021) to employ Kriging interpolation methods. It extracts four types of short-term rainfall (1 hour, 12 hours, 24 hours, 72 hours) and their corresponding long-term rainfall (7 days before the landslide occurrence). In these four threshold models, the distribution of long-term and short-term rainfall thresholds in each group is calculated and then validated using landslide disaster data from 2021. The research results indicate that the prediction accuracy of the four threshold models ranges from 40% to 65%, suggesting good potential for practical application. Additionally, the prediction accuracy improves with the increase in the duration of short-term rainfall. The prediction accuracy for rainfall thresholds calculated from the 72-hour-7-day model is highest, reaching 62%, while the 1-hour-7-day model achieves 46%. Based on the highest prediction accuracy of these models, the study calculates the optimal ratios for short-term and long-term disaster-causing rainfall for four types of models. This leads to a quantitative division between short-term rainfall-induced landslides and long-term rainfall-induced landslides. By calculating the spatial distribution of disaster-causing rainfall, the study extracts rainfall thresholds at potential landslide locations, achieving the goal of one threshold per site in the region and enhancing existing models for calculating rainfall thresholds.