Abstract Over the central United States, a large fraction of heavy precipitation and flood events have been tied to atmospheric rivers (ARs) and to two large-scale atmospheric modes, the Pacific-North American teleconnection pattern (PNA) and the Arctic Oscillation (AO). Here, we build on these insights to model the frequency of heavy precipitation events at 88 locations across the central United States. We use a Bayesian hierarchical modeling framework to develop and compare different models with varying degrees of complexity and nonstationary parameters that are conditioned on ARs and the prevalent climate. We show that Bayesian hierarchical models with a prior layer that allows spatially correlated parameters result in improved prediction skills over the traditional regression-based modeling frameworks. While ARs, PNA, and AO have statistically significant relationships with the frequency of heavy precipitation events over large areas of the central United States, we find that ARs can significantly improve upon the other two covariates in the statistical modeling of extremes over the region.