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 an 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 27 276 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-long 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. 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 that 30 % of
observations from 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 the four benchmark gridded
products and can be used in applications that require either quality-controlled meteorological
station observations or reconstructed long-term estimates for analysis and modeling. The dataset
is available at https://doi.org/10.5281/zenodo.3735533 (Tang et al., 2020).