SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018

Abstract. Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. Motivated by the lack of serially-complete station observations for North America, this study seeks to develop a SCD from 1979 to 2018 from station data. The new SCD for North America (SCDNA) includes daily precipitation, minimum temperature (Tmin), and maximum temperature (Tmax) data for 27280 stations. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico. Stations with at least 8-year records were selected, which underwent location correction and were subjected to strict quality control. Outputs from three reanalysis products (ERA5, JRA-55, and MERRA-2) provided auxiliary information to estimate station records and were also used as an assessment benchmark. Infilling during the observation period and reconstruction beyond the observation period were accomplished by combining estimates from 16 strategies (variants of quantile mapping, spatial interpolation, and machine learning). A sensitivity experiment was conducted by assuming 30 % observations of stations were missing – this enabled independent validation and provided a reference for reconstruction. Quantile mapping and mean-value corrections were applied to the final estimates. The median Kling-Gupta efficiency (KGE) values of the final SCDNA for all stations are 0.90, 0.98, and 0.99 for precipitation, Tmin and Tmax, respectively. The SCDNA is closer to station observations than four benchmark gridded product, and can be used in applications that require either quality-controlled meteorological station observations or reconstructed long-term estimates for analysis and modelling. The dataset is available at https://doi.org/10.5281/zenodo.3735534 (Tang et al., 2020).