1.School of Water Resources and Environment,China University of Geosciences,Beijing,100083;2.China
General Circulation Model (GCM) is one of the main tools to obtain spatial-temporal continuous climate data which can make up for the lack of measurement data in areas where there are no stations. However, the output data of GCM often has certain bias, so it needs to be corrected before use. Taking the Heihe River Basin as the study area and extracting the precipitation data from the BCC-CSM2-MR model in CMIP6 as the source data, this paper mainly focused on evaluating the adaptabilities of different bias correction methods on the extracted monthly precipitation data from 1985-2014 in the study area. The bias correction methods selected in this study include the Linear Scale (LS) method, Empirical Quantile Mapping (EQM) method and Gamma Quantile Mapping (GQM) method, and the evaluation indicators include the Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (R) and Sen"s slope. The GCM precipitation data were compared before and after bias correction with the observed data in the same period. The results showed that: (1) The total index value of precipitation data corrected by LS method was significantly improved, and the NRMSE, MAE and mean error were significantly reduced. However, since the differences in frequency were not taken into account, there was still some errors in the frequency of precipitation data after corrected by LS method. (2) After corrected by EQM and GQM methods, NRMSE and MAE indexes were improved, although the degrees of the improvement were not as large as those from LS method, they were much higher than those before the bias correction. In terms of the frequency index, the results of these two methods were obviously better, and the spatial distributions of precipitation frequencies and variation trends were more consistent with the measured data. In addition, EQM has stronger applicability in this study area according to the indicator values in this paper.