Abstract: Based on monthly precipitation data from CRU TS v4.0 (Climatic Research Unit Timeseries version 4.0), output of the CMIP5 (Coupled Model Intercomparison Project Phase 5) historical experiments and RCP4.5 (Representative Concentration Pathway 4.5) scenario from 24 models, a variety of simple and multiple regression methods were designed to bias-correct projected precipitation for China. These included simple regression (SR), simple regression with log-transformed rainfall (SR-Log), simple regression with year-to-year rainfall increment as predictand (SR-Increment), simple regression with year-to-year log-transformed rainfall increment as predictand (SR-Log-Increment), multiple regression (MR), multiple regression with log-transformed rainfall (MR-Log), multiple regression with year-to-year rainfall increment as predictand (MR-Increment), multiple regression with year-to-year log-transformed rainfall increment as predictand (MR-Log-Increment), and simple removal of climate drift (RCD). Bias-corrected results for projected precipitation over mainland China for 2006-2015 showed that univariate regression correction methods were generally better than multi-variate methods and simple RCD. SR-Log performed best, with rate of precipitation anomaly having the same sign with observation (AR) and precipitation anomaly percentage correlation coefficient (APCC) were the highest, reaching 69% and 0.5, respectively. On the other hand, SR-log-Increment obtained the highest correlation coefficient of precipitation anomaly (ACC) among the different methods. The distributions of precipitation anomaly with the same sign with respect to observation, using different bias-correction methods, showed that the SR-Log performed better in the north than in the south. To the contrary, SR-Increment and SR-Log-Increment performed better in the south than in the north. As a result, the AR, ACC and APCC of the SR-Log or MR-Log were lower than those of the SR-Log-Increment and MR-Log-Increment over southern China (east of 95°E and south of 35°N), while the opposite was true for northern and western China. Therefore, the best regression correction method for model precipitation was regional-dependent, possibly reflecting the differences in statistical properties of precipitation in different regions. Using synthesis of regional regression models, i.e., using SR-Increment in the southern region and SR-Log for the rest of China, the AR of projected precipitation for 2006-2015 improved to 72% while ACC and APCC declined slightly, as the increment regression method increased the variance of the projected precipitation. Projected precipitation for 2016-2045 was bias-corrected by the synthesis of regional regressions method. The results showed that, compared with the average of 1976-2005, the precipitation anomaly pattern for the next 30 years would display a “dry in the north and south, wet in the middle” pattern. Precipitation would decrease by 10%-20% in the middle and lower Yangtze River, middle and west of the regions south of the Yangtze River, the northeastern part of southwestern China, and the coastal regions of southern China and Hainan; precipitation would increase by 10%-40% in the Huai River basin, three rivers source regions, and Taiwan. Minimal changes, or slightly less precipitation was projected over the eastern part of northwestern China, northern China, and most of northeastern China. According to the variance of precipitation anomaly percentage, the spread (uncertainty) of the model group was smaller in the east and larger in the west. It indicated that the projected less precipitation areas were more uncertain such as in the central northwestern, and western Qinghai-Tibet Plateau. In addition, the northern part of the Hetao area, the southern part of northern China, and the eastern part of the south of the Yangtze River corresponded to the “obscured areas,” where the precipitation anomaly in the projections and observations showed opposite signs for the verification period 2006-2015. As such, the projected precipitation over these regions may not be of value. Consequently, alternative methods need to be developed in the future for further improvement.