The traditional evaluation of simulation perfomance for numerical model is based on the differences in average, trend, probability density distribution, extreme value, variance and self-correlation between observational data and the simulated data by numerical model. These evaluation methods mainly depend on the comparison of statistical difference between the simulation and observation, but ignore the comparison in the dynamical characteristics of climate system. In view of this, in the present paper we use detrended fluctuation analysis (DFA) method to analyze the scaling characteristics of daily temperature from observational data and the corresponding simulated data by climate model, and investigate whether the long-range correlation of meteorological elements can exist in the simulated data of climate model. If so, we can assess the performance of climate model based on the comparison of scaling index between the observational data and the simulated data. As an example, the performances of Beijing Climate Center Climate System Model (BCC_CSM1.1(m)) model, including simulated daily average temperature, daily maximum temperature and daily minimum temperature, are assessed by using DFA. The results indicate that the long-range correlation characteristics of temperature in China can be simulated by BCC_CSM1.1(m). But the simulation errors are relatively large in Qinghai-Tibet Plateau, most West China, North China, and Huanghuai district. Thereinto, and the performance of BCC_CSM1.1(m) in Qinghai-Tibet Plateau and western West China is worst in China.