Daily precipitation and temperature for 2021–2050 over China: Multiple RCMs and emission scenarios corrected by a trend‐preserving method

Abstract

Daily climate data are extremely important to drive ecological models, but the outputs of climate models are often deviated from the observations. The bias correction methods mostly highlight the target statistical parameters (e.g., mean and variance) for a target period, but perturb the simulated change trend and variability. In this study, we utilize a trend‐preserving bias correction method that can simultaneously consider the frequency and intensity of climatic variables to adjust the daily maximum and minimum temperature, and precipitation (Tmax, Tmin, and Pre) from CORDEX‐EA regional climate models for the period of 2021–2050 over China. Results show that: (a) the bias correction method greatly improves the spatial patterns of simulated Tmax, Tmin, and Pre since the corrected data has higher correlation and smaller differences across the whole domain. The differences in mean Tmax, Tmin, and Pre between climate models and observations have been reduced from −4.5 ± 6.41°C, −2.06 ± 0.58°C, 177.25 ± 51.55 mm before correction to 0.01 ± 0.02°C, −0.01 ± 0.02°C, −26.22 ± 6.39 mm after correction, respectively. In addition, the simulated change trend and climate variability at all time scales are well preserved. (b) Spatially, the warming rates in the west would be higher than in the east, and they are larger under the most severe future emissions scenario (RCP8.5) relative to the low‐mid emissions scenario (RCP4.5). The Pre change would mainly occur over east China with the significant upward trend. The dataset presented in this article has been published in the Network Common Data Form (NetCDF) File Format at https://doi.org/10.5281/zenodo.5058620 .

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