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Gridded Sub-daily Climate Forcings for North America Based on Daymet and ERA5 (Daymet-ERA5)

    Shih-Chieh Kao | Oak Ridge National Laboratory
    Michele Thornton | Oak Ridge National Laboratory
    Peter E. Thornton | Oak Ridge National Laboratory
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Description

To support high spatial and temporal resolution land surface modeling, this dataset provides hourly time step historic weather forcing at 1-km spatial resolution for the entire North America. The latest Daymet V4 data provides gridded historic daily weather observations at 1-km spatial resolution from 1980 to 2024. Using sub-daily temporal information from the ECMWF ERA5 reanalysis, Daymet was further temporally downscaled to hourly time steps and provided in the format required for land surface model simulations. The process of temporal downscaling preserves the relative magnitude in each hourly time step from ERA5 while maintaining the total and average values from Daymet for each day. This results in a blended 1980-2024 Daymet-ERA5 dataset. Available variables include surface air temperature, precipitation, specific humidity, shortwave and longwave radiation, wind speed, and pressure. These data can be used as a high-resolution meteorological forcing dataset to support high-resolution land surface modeling where accurate meteorological forcing datasets built from historic observations and/or reanalysis datasets are desirable.

Funding Information

DOE Contract Number

AC05-00OR22725

Originating Research Organization

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization

Office of Science (SC)

Related Works

Details

Release Date

May 19, 2026

Subject

54 ENVIRONMENTAL SCIENCES

Keywords

Daymet, Earth System Modeling, Subdaily Forcings, Surface Weather, ERA5

Dataset

Dataset Type

ND Numeric Data

Software

Any type of NetCDF readers.

Cite This Dataset:

Kao, S., Thornton, M., Thornton, P. (2026). Gridded Sub-daily Climate Forcings for North America Based on Daymet and ERA5 (Daymet-ERA5). Oak Ridge National Laboratory. https://doi.org/10.13139/OLCF/3028406.

Acknowledgements

This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Advanced Scientific Computing Research programs in the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.