Serially Complete Station Precipitation and Temperature Data for CONUS (1950-2023)
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Description: This dataset provides gap-filled and reconstructed daily station observations of precipitation and temperature across the Contiguous United States (CONUS) from 1950 to 2023. It is derived from high-density networks (GHCN-D and MADIS) using a multi-strategy reconstruction approach, including quantile mapping, interpolation, machine learning, and merging methods. The dataset contains 24,727 precipitation stations and 14,694 temperature stations with no missing values, offering a spatially and temporally complete foundation for climate analysis, hydrological modeling, and ensemble estimation. Note the number of stations is a bit smaller than that used in the relevant publication because MADIS stations are excluded due to its restriction regarding data distribution. The relevant ensemble gridded precipitation and temperature dataset is available on NCAR GDEX. Key Features: Variables: Daily precipitation, mean temperature (Tmean), and temperature range (Trange) Temporal coverage: 1950-01-01 to 2023-12-31 Spatial coverage: CONUS and surrounding regions Station count: 25,887 precipitation stations, 20,998 temperature stations Data quality: High accuracy (median KGE″: 0.89 for precipitation, 0.99 for Tmean, 0.93 for Trange) Format: NetCDF (CF-compliant) Citation: If you use this dataset, please cite the following publications: Tang, G., Wood, A. W., Newman, A. J., Kirstetter, P. E., Mueller, C., & Frans, C. (2025). High-resolution ensemble precipitation and temperature datasets for CONUS based on a probabilistic geospatial estimation approach. Journal of Hydrology, 134761. Tang, G., Clark, M. P., & Papalexiou, S. M. (2021). SC-Earth: A station-based serially complete Earth dataset from 1950 to 2019. Journal of Climate, 34(16), 6493-6511. Contact:Guoqiang Tang (guoqiang.tang@whu.edu.cn)Andy Wood (andywood@ucar.edu) Acknowledgement: This study is supported primarily by research grants from NOAA's Climate Program Office (Award #s NA23OAR4310448 and NA23OAR4310446) with additional support from the National Natural Science Foundation of China (U2340213) and the United States Army Corps of Engineers (the ‘Robust Hydrology’ projects). We also acknowledge the high-performance computing support from the Derecho system (doi:10.5065/qx9a-pg09) that is managed by the National Science Foundation (NSF)-sponsored National Center for Atmospheric Research (NCAR). This material is also based upon work supported by the NSF NCAR, which is a major facility sponsored by the U.S. National Science Foundation under Cooperative Agreement No. 1852977.
