NEW: This dataset has now been moved from Microsoft OneDrive to an AWS S3 bucket for your convenience in downloading. Please use the download code we provided to pull data from the AWS S3 bucket without login credentials. This dataset is from the high-resolution, multiscale, differentiable HBV hydrologic models, provided by the Multi-scale Hydrology, Processes, and Intelligence (MHPI) team from The Pennsylvania State University, led by Dr. Chaopeng Shen's group in Hydrologic Deep Learning and Modeling. Please cite: Song, Y., Bindas, T., Shen, C., Ji, H., Knoben, W. J. M., Lonzarich, L., et al. (2025). High‐resolution national‐scale water modeling is enhanced by multiscale differentiable physics‐informed machine learning. Water Resources Research, 61, e2024WR038928. https://doi.org/10.1029/2024WR038928 The dataset is generated by High-resolution, multiscale, differentiable HBV hydrologic models, dHBV2.0UH and dHBV2.0dMC. dHBV2.0UH is a high-resolution, multiscale model that uses unit hydrograph routing. dHBV2.0dMC is a high-resolution, multiscale model that uses external Muskingum-Cunge routing. Please use the link in the Readme.docx file to access the dataset. A link to the code for loading the dataset is also provided in Readme.docx. The dHBV_streamflow_simulation_gages folder includes 40 years (1980–2020) of streamflow simulations at over 7,000 gage stations from GAGES-II, using both dHBV2.0UH and dHBV2.0dMC models. This data is useful for comparison with observations. The MERIT_flux_states folder includes 40 years (1980–2020) of spatially seamless simulations of hydrologic variables over 180 thousand MERIT unit basins on CONUS from dHBV2.0UH, including baseflow, evapotranspiration (ET), soil moisture, snow water equivalent, and runoff. The dHBV2.0_MERIT_river_network_simulation includes 40 years (1980-2020) of streamflow simulation on seamless MERIT river network by dHBV2.0dMC (New updates!). The dHBV2.0UH code is available at mhpi/generic_deltaModel: High-resolution differentiable model, 𝛿HBV2.0. https://doi.org/10.5281/zenodo.14827983