Abstract: The ability of the 17 CMIP5 models in simulating the ENSO phenomenon is examined by using the outputs of these models from the historical experiments of the 20th century. In general, the models can simulate some major characteristics of the ENSO phenomena, such as the mean sea surface temperature (SST) in the tropical Pacific; the temporal and spatial evolution of the SST anomalies; the interactive relation between oceans and the atmosphere; the periodicity of the ENSO; the phase locking feature of the ENSO and so on. There is large difference in the ability of simulating the ENSO between various models. (1) The simulated SST still has some errors in various degrees. This error is small for the multiple model ensemble with the root mean square error (RMSE) between the simulated and observed SST being below 1.0℃ and otherwise for each single model in which the RMSE is larger than this. Some good models can have error of 1.2-1.3℃, majority of the models has errors below 1.6℃, and there are still few models which have RMSE exceeding 2.0℃. (2) According to the Empirical Othorgnal Function (EOF) analyses, the temporal and spatial variation of the simulated SST anomalies and Sea Level Pressure (SLP) anomalies for a few of the models is close to the observation, its first mode is ENSO mode and the corresponding time coefficient represents the ENSO evolution. Its second mode represents the increasing trend of the SST anomaly during the last period of more than 50 years. For most of the models the sequence of the temporal and spatial variation mode of the simulated SST/SLP anomaly is opposite to that of the observation. The increasing trend becomes the first mode with the major variance contribution, while the ENSO becomes the second mode. This means that the mechanism which produces the temperature increase from the CO2 induced greenhouse effect is too strong in these models, while the ENSO oscillation mechanism is rather weak. It is showed that no matter it is the first mode or second mode, the corresponding time coefficient of the southern oscillation has the good correlation with the SST anomaly. This means the CMIP5 models can well represent the close relationship between the El Nio-La Nia and the southern oscillation. (3) The spectral analysis shows that the ENSO phenomenon has 2-7 year quasi-periodicity and the 4 year periodicity is the most obvious. In most of the CMIP5 models the ENSO has the periods of 2-7 years, this is consistent to the observation. But some models have ENSO period of 2 year or so, and few has too long period of 11 years. And, (4) in the simulations of most of models the peak phase of the El Nio/La Nia appears in later fall through winter (November-February), which is consistent to observation. There are also few models whose simulated ENSO peak appears in September-October or even in summer and this is not consistent with observation.