Metrics that approximate hillslope hydrologic response to rainfall are fundamental for informing landslide risk reduction efforts, such as early warning systems and hazard models. Notwithstanding the numerous publications using different wetness metrics that underpin and largely control the accuracy of landslide risk reduction products, a robust comparison of a broad array of different wetness metrics for regional landslide analysis is currently lacking in the literature. In this study, we statistically compare common wetness metrics for predicting the temporal occurrence of rainfall-triggered landslides at regional scales (> 1000 km<sup>2</sup>) using a landslide inventory covering the contiguous United States. We find that representations of hillslope wetting and drainage using parsimonious leaky bucket models that only require rainfall input and an estimated drainage factor can identify landslide-triggering hydrologic conditions across disparate ecological regions more accurately than unmodified precipitation metrics or more complex hydrological models. Due to the proliferation of global precipitation datasets and the limited input data needed for the parsimonious leaky bucket model, this model could be used to improve tools for landslide risk reduction.