We investigate the links between multidecadal changes in extreme sea levels (expressed as 100 year return water levels (RWLs)) along the United States coastline and large‐scale climate variability. We develop different sets of simple and multiple linear regression models using both traditional climate indices and tailored indices based on nearby atmospheric/oceanic variables (winds, pressure, sea surface temperature) as independent predictors. The models, after being tested for spatial and temporal stability, are capable of explaining large fractions of the observed variability, up to 96% at individual sites and more than 80% on average across the region. Using the model predictions as covariates in a quasi nonstationary extreme value analysis also significantly reduces the range of change in the 100 year RWLs over time, turning a nonstationary process into a stationary one. This suggests that the models—when used with regional and global climate model output of the predictors—will also be capable of projecting future RWL changes. Such information is highly relevant for decision makers in the climate adaptation context in addition to projections of long‐term sea level rise.