Based on the observed data, the performances of bias-corrected CMIP6 General Circulation Models (GCMs) for simulating a meteorological drought over China were evaluated. The models are archived at the Program on Climate Model Diagnosis and Intercomparison (PCMDI) website (https://pcmdi.llnl.gov/index.html, accessed on 10 January 2021). Limited by the daily scale CMIP6 data, 15 climate models were selected in this research (Table 1). In this research, data sets were selected as daily data. We first processed daily scale data into monthly scale data and then calculated the meteorological drought index (SC-PDSI). The reason for selecting daily scale data is to lay the foundation for further research and the CMIP6 daily dataset provides more possibilities for drought research, such as hydrological drought and its connection with meteorological drought. The historical period of CMIP6 data covers 54 years, from 1961 to 2014; therefore, the future period is selected to be from 2021 to 2074. Considering future conditions and global warming signals, four Shared Socioeconomic Pathways (SSP) scenarios (SSP126, SSP245, SSP370 and SSP585) with low, medium and high future greenhouse gas emissions were simulated and compared. All model outputs were first bilinearly-interpolated to the same 10 km × 10 km grid as the observations for the different spatial resolutions of different GCMs. In addition to precipitation and temperature data, the soil available water capacity is also required to calculate the SC-PDSI and the soil available water capacity data were obtained from the available water capacity dataset provided by Webb et al. (2000) in globally gridded digital format.