El Niño and La Niña events influence weather patterns across the globe. Now, scientists have identified the conditions necessary to predict some of those events a year in advance, potentially increasing the crucial time needed to prepare for weather extremes.
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El Niño has been hitting headlines for much of 2026, with scientists warning of a historically strong ‘super’ El Niño. El Niño is one state (or phase) of a natural climate pattern, known as the El Niño-Southern Oscillation (ENSO), which involves fluctuating ocean temperatures in the central and eastern equatorial Pacific as well as changes in the atmosphere.
ENSO has three states: El Niño, characterised by unusually warm ocean temperatures; La Niña, characterised by cold ocean temperatures; and the neutral state, when the temperatures tend to remain near their long-term averages. These phases influence weather patterns around the globe, bringing powerful storms, heavy rainfall and droughts to different parts of the planet.
Predicting El Niño
Unfortunately, these phases don’t run like clockwork: El Niño comes around roughly every two to seven years and typically lasts up to 18 months. La Niña can stretch for up to three years. They also don’t follow a fixed sequence, transitioning randomly between any of the three phases.
Both El Niño and La Niña typically peak during the northern hemisphere winter, around November or December, extending into the next couple of months. So far, scientists have been able to forecast these events with confidence around six months in advance.
Researchers from the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the University of Melbourne, Australia, wanted to know whether the ENSO phase at the time of prediction influences the accuracy of the forecast. So, they analysed more than 7,500 historical ENSO forecasts from climate models developed in nine countries, focusing on just that.
The findings, published in the journal Science Advances, suggest that when you forecast matters more than which model you use. The study found that forecasts initiated during La Niña conditions consistently performed well when predicting ENSO events a year ahead. Three models correctly predicted transitions to El Niño a year in advance more than 70 per cent of the time, while forecasts made during the neutral phase were less reliable.
“Our study identifies valuable additional information for predicting El Niño and La Niña events well ahead of time,” says co-author Mandy Freund, who is a professor at the University of Melbourne.
“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.”
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The research also revealed that different forecasting approaches might suit different climate conditions better. There are four primary types of models that scientists use to predict ENSO events: dynamical (physics-based numerical equations); statistical (mathematical relationships derived from historical datasets); machine learning/deep learning (advanced neural networks that capture complex patterns); and hybrid models.
According to the study, traditional physics-based models struggled when forecasts began from neutral conditions, but machine learning and statistical models to improve performance.
Future use
These results can be used to decide when to communicate forecasts and warnings of ENSO events, and to determine the level of confidence to assign to these warnings. For example, during an ongoing La Niña, predictions of next year’s event could be communicated to the public, but during neutral states, it might be more useful to wait until later in the year.
The researchers acknowledge that their analysis doesn’t address complexities such as long-term climate change, natural decade-to-decade shifts, or the fact that El Niño and La Niña events can vary considerably. Further research is needed to build upon their findings.
“We are still a few steps away from applying our findings to the models used in practice by forecasting agencies,” says Nandini Ramesh, a senior research scientist at CSIRO and lead author of the study.
“But these results hold promise for being able to communicate early warnings for future events.”
Read the full findings published in the journal Science Advances.
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Top image: sea wave during storm in the Atlantic ocean. Credit: andrej67/Getty Images
