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In land surface models (LSMs), the hydraulic properties of the subsurface are commonly estimated according to the texture of soils at the Earth's surface. This approach ignores macropores, fracture flow, heterogeneity, and the effects of variable distribution of water in the subsurface oneffectivewatershed‐scale hydraulic variables. Using hydrograph recession analysis, we empirically constrain estimates of watershed‐scale effective hydraulic conductivities (K) and effective drainable aquifer storages (S) of all reference watersheds in the conterminous United States for which sufficient streamflow data are available (n = 1,561). Then, we use machine learning methods to model these properties across the entire conterminous United States. Model validation results in high confidence for estimates of log(K) (r² > 0.89; 1% < bias < 9%) and reasonable confidence forS(r² > 0.83; −70% < bias < −18%). Our estimates of effectiveKare, on average, two orders of magnitude higher than comparable soil‐texture‐based estimates of averageK, confirming the importance of soil structure and preferential flow pathways at the watershed scale. Our estimates of effectiveScompare favorably with recent global estimates of mobile groundwater and are spatially heterogeneous (5–3,355 mm). Because estimates ofSare much lower than the global maximums generally used in LSMs (e.g., 5,000 mm in Noah‐MP), they may serve both to limit model spin‐up time and to constrain model parameters to more realistic values. These results represent the first attempt to constrain estimates of watershed‐scale effective hydraulic variables that are necessary for the implementation of LSMs for the entire conterminous United States.