The tropical Pacific is currently experiencing an El Nio event. Various coupled models with different degrees of complexity have been used to make real-time El Nio predictions, but large uncertainties exist in the intensity forecast and are strongly model dependent. An intermediate coupled model(ICM) is used at the Institute of Oceanology, Chinese Academy of Sciences(IOCAS),named the IOCAS ICM, to predict the sea surface temperature(SST) evolution in the tropical Pacific during the2015–2016 El Nio event. One unique feature of the IOCAS ICM is the way in which the temperature of subsurface water entrained in the mixed layer(T_e) is parameterized. Observed SST anomalies are only field that is utilized to initialize the coupled prediction using the IOCAS ICM. Examples are given of the model’s ability to predict the SST conditions in a real-time manner. As is commonly evident in El Nio Southern Oscillation predictions using coupled models,large discrepancies occur between the observed and predicted SST anomalies in spring 2015. Starting from early summer 2015, the model can realistically predict warming conditions. Thereafter, good predictions can be made through the summer and fall seasons of 2015. A transition to normal and cold conditions is predicted to occur in late spring 2016. Comparisons with other model predictions are made and factors influencing the prediction performance of the IOCAS ICM are also discussed. The tropical Pacific is currently experiencing an El Nio event. Various coupled models with different degrees of complexity have been used to make real-time El Nio predictions, but large uncertainties exist in the intensity forecast and are strongly model dependent. An intermediate coupled model (ICM) is used at the Institute of Oceanology, Chinese Academy of Sciences (IOCAS), named the IOCAS ICM, to predict the sea surface temperature (SST) evolution in the tropical Pacific during the 2015-2016 El Nio event One unique feature of the IOCAS ICM is the way in which the temperature of subsurface water entrained in the mixed layer (T_e) is parameterized. Observed SST anomalies are only field that is utilized to initialize the coupled prediction using the IOCAS ICM. Examples are given of the model’s ability to predict the SST conditions in a real-time manner. As is generally evident in El Nio Southern Oscillation predictions using coupled models, large discrepancies occur between the observed and predicted SST anomalies in spring 2015. Starting from early summer 2015, the model predictive can be made through the summer and fall seasons of 2015. A transition to normal and cold conditions is predicted to occur in late spring 2016. Comparisons with other model predictions are made and factors influencing the prediction performance of the IOCAS ICM are also discussed.