Abstract: Daily minimum temperature datasets of observation stations, the NCEP reanalysis data were used in this study to assess the BCC-CSM1.1 modeling data over China. Firstly, the grid data were downscaled to observational stations with the Optimal Points Regression (OPR) method, and then China’s regional low temperature events were identified from the three datasets using the Objective Identification Technique for Regional Extreme Events (OITREE). Results show that the NCEP reanalysis data show very good consistency with the observational daily minimum temperatures, while the BCC-CSM1.1 generally also shows a good ability in modeling the daily minimum temperatures, although its modeled data are not as good as the NCEP reanalysis data. For China’s regional low temperature events, the NCEP reanalysis data show very good consistency with the observations in the long-term trend and interannual variation, and the spatial distributions of accumulated intensity and frequency. Meanwhile, the BCC-CSM1.1 simulations show good consistency with the observations in the decreasing tendencies in frequency, extreme intensity, maximum impacted area, duration, accumulated impacted area and integrated index of China’s regional low temperature events during 1961-2005. In addition, the BCC-CSM1.1 model overall shows good ability in modeling the main characteristics of the spatial distributions of the accumulated intensity and frequency of China’s regional low temperature events, but relatively poor ability in modeling the locations of the high-value centers.

Key words: BCC-CSM1.1 model, China, regional low temperature event, numerical simulation, comparison research