Meteorological data from ground stations suffer from temporal discontinuities caused by missing values and short measurement periods. Gap filling and reconstruction techniques have proven to be effective in producing serially complete station datasets that are used for a myriad of meteorological applications (e.g., developing gridded meteorological datasets and validating models). We developed the serially complete Earth (SC-Earth) dataset, which provides global station-based daily precipitation, mean temperature, temperature range, dew-point temperature, and wind speed data from 1950 to 2019. SC-Earth utilizes raw station data from the Global Historical Climatology Network-Daily (GHCN-D) and the Global Surface Summary of the Day (GSOD). The five variables are precipitation (prcp), mean daily temperature (tmean), daily temperature range (trange), dew-point temperature (tdew), and wind speed (wind). Daily minimum and maximum temperature can be inferred from tmean and trange. Humidity variables can be inferred from tdew. There are three files for each variable. "observation" contains quality controlled raw station observations. "estimate" contains SC-Earth estimates for all days (including days that "observation" has values) by merging estimates from 15 strategies (quantile mapping, interpolation, machine learning, and multiple-strategy merging). "final" is the final SC-Earth output, which uses "estimate" to fill the gap that "observation" is not available. Reference: Guoqiang Tang, Martyn P. Clark, Simon Michael Papalexiou. (2021). SC-Earth: A Station-based Serially Complete Earth Dataset from 1950 to 2019. Journal of Climate.