Global Climate Models (GCM) are the primary tools for studying past climate change and evaluating the projected future response of climate system to changing atmospheric composition. However, the stateofart GCMs contain large biases in regional or local scales and are often characterized by low resolution which is too coarse to provide the regionalscale information required for regional climate change impact assessment. A popular technique, Bias Correction and Spatial Disaggregation (BCSD), are widespreadly employed to improve the quality of the raw model output and downscaling throughout the world. Unfortunately, this method has not been applied in China. Consequently, the detailed principle and procedure of BCSD are introduced systematically in this study. Furthermore, the applicability of BCSD over China is also examined based on an ensemble of climate models from phase five of the Coupled Model Intercomparison Project (CMIP5), though the excellent performance of it has been validated for other parts of the world in many works. The result shows that BCSD is an effective, modelindependent approach to removing biases of model and downscaling. Finally, application scope of BCSD is discussed, and a suite of fineresolution multimodel climate projections over China is developed based on 34 climate models and two emissions scenarios (RCP4.5 and RCP8.5) from CMIP5.