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

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, T-min, and T-max, 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 doi.org/10.5281/zenodo.3735533 (Tang et al., 2020).