Originating in the equatorial Pacific, the El Niño-Southern Oscillation (ENSO) has highly consequential global impacts, motivating the need to understand its responses to anthropogenic warming. In this Review, we synthesize advances in observed and projected changes of multiple aspects of ENSO, including the processes behind such changes. As in previous syntheses, there is an inter-model consensus of an increase in future ENSO rainfall variability. Now, however, it is apparent that models that best capture key ENSO dynamics also tend to project an increase in future ENSO sea surface temperature variability and, thereby, ENSO magnitude under greenhouse warming, as well as an eastward shift and intensification of ENSO-related atmospheric teleconnections -the Pacific-North American and Pacific-South American patterns. Such projected changes are consistent with palaeoclimate evidence of stronger ENSO variability since the 1950s compared with past centuries. The increase in ENSO variability, though underpinned by increased equatorial Pacific upper-ocean stratification, is strongly influenced by internal variability, raising issues about its quantifiability and detectability. Yet, ongoing coordinated community efforts and computational advances are enabling long-simulation, large-ensemble experiments and high-resolution modelling, offering encouraging prospects for alleviating model biases, incorporating fundamental dynamical processes and reducing uncertainties in projections.

This study develops a new observational constraint method, called multimodel ensemble pattern regression (EPR), to correct the projections of regional climate change by the conventional unweighted multimodel mean (MMM). The EPR method first extracts leading modes of historical bias using intermodel EOF analysis, then builds up the linear correlated modes between historical bias and change bias using multivariant linear regression, and finally estimates the common change bias induced by common historical bias. Along with correcting common change bias, the EPR method implicitly removes the intermodel uncertainty in the change projection deriving from the intermodel diversity in background simulation. The EPR method is applied to correct the patterns of tropical Pacific SST changes using the historical and representative concentration pathway 8.5 (RCP8.5) runs in 30 models from phase 5 of CMIP (CMIP5) and observed SSTs. The common bias patterns of the tropical Pacific SSTs in historical runs, including the excessive cold tongue, the southeastern warm bias, and the narrower warm pool, are estimated to induce La Niña–like change biases. After the estimated common change biases are removed, the corrected SST changes display a pronounced El Niño–like pattern and have much greater zonal gradients. The bias correction decreases by around half of the intermodel uncertainties in the MMM SST projections. The patterns of corrected tropical precipitation and circulation change are dominated by the enhanced SST change patterns, displaying a pronounced warmer-get-wetter pattern and a decreased Walker circulation with decreased uncertainties.