Rainfall-induced landslide early warning systems require reliable estimation of soil moisture conditions. This study proposes a Soil Water Index (SWI) framework based on a three-stage TANK model. Through GLUE (Generalized Likelihood Uncertainty Estimation)-based behavioral parameter sampling and K-means clustering, SWI response characteristics were classified into two representative scenarios: slow drainage (Scenario 1) and fast drainage (Scenario 2). Two-stage thresholds—Watch (α = 0.40 × SWI_peak) and Warning (β = 0.70 × SWI_peak)—were established from SWI rise profile analysis at 500 m and 5 km resolutions, providing 20–27 and 4–5 h of lead time, respectively. Verification against the July 2025 heavy rainfall event across multiple resolutions and spatial extents yielded Hit Rates of 0.984–1.000, while FAR (False Alarm Ratio) remained structurally high (0.607–0.648 for grids sharing the rainfall field with occurrence sites). These findings confirm that SWI serves as an effective regional-scale necessary condition indicator for landslide-triggering moisture, but FAR reduction requires integration with slope susceptibility information.