This dataset has been superseded by our new dataset, CHM_PRE_SSP, a bias-corrected daily 0.1° precipitation dataset for China. Please use CHM_PRE_SSP for future applications: CHM_PRE_SSP, a new bias-corrected daily CMIP6 precipitation projection dataset over the Chinese mainland

1. Description

The dataset is generated by bias-correcting the CMIP6 daily precipitation data using precipitation observations from over 2,400 stations in China. This results in daily precipitation data with a 0.5° resolution for China from 2015 to 2100. The brief process of data generation is as follows:

(1) Observational data:

The observational data are based on daily precipitation records from 2,400 meteorological stations across China. Using inverse distance weighting (IDW) interpolation, these station data were transformed into gridded daily precipitation datasets with a spatial resolution of 0.5°×0.5°, covering the period from 1960 to 2014.

(2) Model data:

We used outputs from 22 CMIP6 models, including both historical simulations (1960–2014) and projections under four future scenarios (2015–2100: SSP126, SSP245, SSP370, and SSP585). All model data were interpolated using bilinear interpolation and standardized to a spatial resolution of 0.5°×0.5°.

(3) Bias correction methods:

The bias correction process first addressed the wet day frequency. Specifically, based on comparisons between observed and modeled precipitation frequencies from 1960 to 2014, the wet day frequency of each CMIP6 model was adjusted during the period 2015–2100. Following this adjustment, daily precipitation values from 2015 to 2100 were then bias-corrected using cumulative distribution function (CDF) matching. This procedure considered the nonstationary characteristics of the precipitation distribution over time. The CDFs were estimated using the empirical cumulative distribution function method.

2. Content of the dataset

This dataset consists of 88 files, corresponding to daily precipitation data from 22 CMIP6 models under 4 different scenarios. The data is stored in standard NETCDF format. The file name is structured as “BC_pr_day_MODEL_SSP_gn_chn_2015_2100.nc”, where BC refers to bias correction, pr_day indicates daily precipitation, MODEL represents the model’s name, and SSP denotes the scenario. For example: BC_pr_day_MODEL_SSP_gn_chn_2015_2100.nc. Each data file is approximately 350 MB in size. All NETCDF files in this dataset can be opened using standard nc processing tools such as MATLAB, Python, Fortran, Panoply, etc.

3. Details of the variables in the file

Each NetCDF file contains the following four variables:

(1) lat: Latitude dimension, measured in degrees (°).

(2) lon: Longitude dimension, measured in degrees (°).

(3) time: Time dimension, measured in days since January 1, 2015.

(4) pr: Precipitation variable with dimensions (lon, lat, time). The unit of this variable is mm/day.

4. Resolution and Data Range

Resolution: 0.5°.

Time frame: January 1, 2015, to December 31, 2100.

Space scope: 18°N–54°N, 72°E–136°E (as detailed in the table below).

5. Authors and contacts

Chiyuan Miao (miaocy@bnu.edu.cn)

Jinlong Hu (hujl98@mail.bnu.edu.cn)