Decadal variability is an oscillation for more than ten years, usually appears in the ocean. In the Atlantic, AMO is the main multi-decadal variability. There are several decadal oscillations in the Pacific, which we call Pacific decadal variability (PDV). These decadal structures may impact on regional climate. IPCC will take decadal variability as an important issue and publish the result in the fifth assessment report (AR5). This study uses Rotated-EOF (REOF) method, analyzes decadal variability in observation data and CMIP5 models. In observation data, AMO structure appears in REOF1. Decadal variability of Nino region, north Pacific and Indian Ocean are the 2nd mode in REOF analysis. SPDO, PDO and decadal variability in central Pacific appear in REOF3. The rotated principal components (RPCs) also match with the previous study. In CMIP5 models, we use three different model experiment designs: (1) Pre-industrial, refer as the internal variability in models. (2) Historical run, with the observation record from 1850 to 2005. (3) RCP8.5 identifies a concentration pathway that approximately results in a radiative forcing of 8.5 W/m2 at year 2100. In the pre-industrial run, most of the CMIP5 models show the decadal variability of El-Nino region, but the AMO structure which consider as a nature mode in decadal variability, does not appear in model’s pre-industrual run. In the Historical run, decadal structure shows variety types in each model. In the model’s RCP8.5 run, main decadal structure shows large region in global, with an upward parabola time series. We use pattern correlation analysis, and choose the models which are similar with observation data. In this study, historical run in HadGEM2-CC models are similar with the observation data. On the analysis of variance, we separate total variance to three different time scales: annual, decadal and trend. We compute the ratio of the three time scales and the total variance. The smallest ratio of trend variance is model’s pre-industrial run, but most significant in RCP8.5 run which the mean is more than 80% in box-plot analysis. In variance map, decadal variance ratio is most significant in North Hemisphere Ocean in observation data; it can be simulated in some model’s historical run. In RCP8.5 run, the decadal variability ratio is less than 10% in most regions. We find that the total mass of CO2 in RCP8.5 run, ascending with a parabolic curve. We remove the linear trend of total mass of CO2, the residual forcing could project on decadal variability and affect the RPC performance. Keywords: Decadal variability, Atlantic Multi-decadal Oscillation, pacific decadal variability, rotated-EOF, pattern correlation