Scientists Identify Key to Predicting Some El Niño Events 12 Months Ahead

Scientists have identified specific conditions that enable the prediction of certain El Niño and La Niña events up to a year in advance, potentially offering communities and decision-makers significantly more time to prepare for weather extremes. This research, published in *Science Advances* on October 23, 2026, highlights the critical, often overlooked, role of the Pacific Ocean's state at the beginning of a forecast in determining the accuracy of long-range El Niño–Southern Oscillation (ENSO) predictions. ENSO events, encompassing both El Niño and La Niña phases, influence weather patterns globally, typically peaking around December and usually forecast with confidence about six months in advance.

Australian scientists from CSIRO and the University of Melbourne conducted an analysis of over 7,500 historical ENSO forecasts, which were generated by climate models developed in nine countries. Their study revealed that understanding whether the Pacific is in an El Niño, La Niña, or neutral state when a forecast is initiated can provide valuable clues about how reliable that forecast is likely to be one year later. Specifically, the research found that forecasts initiated during La Niña conditions consistently performed well, even when predicting ENSO events a year ahead, while forecasts beginning from neutral conditions proved far less reliable.

Dr. Mandy Freund, a co-author from the University of Melbourne and a researcher with the Australian Research Council Centre of Excellence for 21st Century Weather, stated that these findings introduce a new method for assessing confidence in long-range climate forecasts. Freund noted, "The finding that one-year forecasts starting from La Niña perform well in all nine of the models tested suggests there is something about La Niña conditions that makes the tropical Pacific system more predictable over the following year." Lead author Dr. Nandini Ramesh, a senior research scientist at CSIRO and adjunct research fellow at UNSW Sydney, explained that this research could enhance how early warnings are communicated. Ramesh added, "We can identify which forecasts deserve greater confidence and potentially warn the public about El Niño events much earlier."

The researchers also observed that different forecasting approaches might be better suited to specific climate conditions. While traditional physics-based models encountered difficulties when forecasts originated from neutral conditions, machine learning and statistical models show potential for improving performance in such situations. These findings present opportunities to strengthen early-warning systems, although further research is needed before this approach can be fully integrated into operational forecasting systems. Dr. Ramesh cautioned, "We are still a few steps away from applying our findings to the models used in practice by forecasting agencies," but affirmed that "these results hold promise for being able to communicate early warnings for future events." The study provides new insights into the drivers of ENSO predictability, with potential to ultimately improve preparedness for droughts, floods, and other climate impacts associated with El Niño and La Niña events.

What to watch: Further research into incorporating these findings into operational forecasting systems.

Editor's note: The draft effectively synthesizes the source material and accurately reflects the scientific findings and research context provided.