Abstract: The 2015/2016 El Niño event has been widely accepted as a super event that is comparable to the two events of 1982/1983 and 1997/1998. In the present study, some main features of upper ocean variables during this new super event were compared with those during the two historical super events, and the associated major physical processes were quantitatively diagnosed based on the tropical-Pacific mixed-layer heat budget equation. Results show that during the early stage of this new event, there existed an eastward propagation of Sea Surface Temperature (SST) anomalies and a weak warm-pool El Niño event. However, this super event was featured by a weak westward propagation during its mature phase, when the SST anomaly center shifted westward due to strong easterly wind and cold upwelling anomalies as well as the westward-propagating anomalies of zonal current and subsurface ocean temperature. Cold water invasion in the eastern boundary of the equatorial eastern Pacific weakened SST anomalies there, which was the direct reason for the westward shift of this event. Heat budget analysis suggested that the thermocline feedback was the most important process that induced the SST anomaly increase and phase transition of this new super event, while the zonal advection feedback also played an important role in the formation of the strong warming and westward shift of SST anomalies. During this event, several westerly wind burst (WWB) events occurred and the oceanic Kelvin waves propagated eastward before they basically maintained over the eastern Pacific during the mature period, while there were no evident westward propagation of oceanic Rossby waves over the off-equatorial region. There was an obvious discharging process of the equatorial heat during the developing and mature phases. In addition, the new generation El Niño-Southern Oscillation prediction system (SEMAP2.0) developed in National Climate Center of China yielded reasonable forecasts, which were presented in the two real-time operational discussion meetings during 2014-2016. In particular, the statistical prediction model that considered the preceding variation information of oceanic factors provided better forecasts for the fluctuation and evolution of SST anomaly, and became an important member of multi-method ensemble.