Natural and human-related landscape features influence the ecology and water quality of lakes. Summarizing these features in a hydrologically meaningful way is critical to understanding and managing lake ecosystems. Such summaries are often done by delineating watershed boundaries of individual lakes. However, many technical challenges are associated with delineating hundreds or thousands of lake watersheds at broad spatial extents. These challenges can limit the application of analyses and models to new, unsampled locations. We present the Lake-Catchment (LakeCat) Dataset (https://www.epa.gov/national-aquatic-resource-surveys/lakecat) of watershed features for 378,088 lakes within the conterminous USA. We describe the methods we used to: 1) delineate lake catchments, 2) hydrologically connect nested lake catchments, and 3) generate several hundred watershed-level metrics that summarize both natural (e.g., soils, geology, climate, and land cover) and anthropogenic (e.g., urbanization, agriculture, and mines) features. We illustrate how this data set can be used with a random forest model to predict the probability of lake eutrophication by combining LakeCat with data from US Environmental Protection Agency’s National Lakes Assessment (NLA). This model correctly predicted the trophic state of 72% of NLA lakes, and we applied the model to predict the probability of eutrophication at 297,071 unsampled lakes across the conterminous USA. The large suite of LakeCat metrics could be used to improve analyses of lakes at broad spatial extents, improve the applicability of analyses to unsampled lakes, and ultimately improve the management of these important ecosystems.