ENSO‐Conditioned Evolution of Global Mean Surface Temperature

Abstract Here we examined how the June–May trajectory of global mean surface temperature (GMST) can be anticipated from recent GMST evolution and upcoming boreal winter Niño‐3.4 values. Principal component analysis and dimension reduction led to a simple, interpretable model in which the June–May monthly GMST trajectory is conditioned on two quantities: the average GMST of the prior 12 months and the upcoming December value of Niño‐3.4. Including ENSO improves performance relative to persistence baselines that do not include ENSO, especially during times of the year when atmospheric bridge mechanisms are active. The model associates a December Niño‐3.4 anomaly of 1°C with a GMST increase of about 0.03°C during June–August followed by a peak increase of about 0.11°C in the following February. In comparison, initialized climate forecasts show broadly similar ENSO–GMST relationships and provide additional skill prior to the ENSO peak.