For the past seven months, experts have closely monitored rising sea surface temperatures in the tropical Pacific, signaling the approach of what is being described as a “Godzilla” El Niño—the strongest on record in absolute terms. This naturally recurring climate event emerges when trade winds weaken, allowing warm waters to spread eastward, disrupting weather patterns globally. The consequences can include damaged harvests, strained energy supplies, and disrupted shipping routes.

Giancarlo Pini, an earth observation specialist with the United Nations World Food Programme (WFP), has been tracking this development and emphasizes its historic scale. Even before the event’s official declaration in June, Pini's office had begun issuing warnings about significant impacts on food security, projecting that by the end of 2027, 274 million more people across 45 countries could experience acute food shortages. A separate study from the Climate Impact Lab anticipates at least 451,000 heat-related deaths connected to the phenomenon, with Nigeria, Indonesia, and Sudan among the hardest hit.

To better anticipate and mitigate these impacts, various organizations and researchers are turning to artificial intelligence (AI) for weather forecasting. AI offers the potential for faster, less expensive analysis and broader dissemination of predictive information. For instance, the WFP integrates AI models with traditional weather forecasts to provide early warnings to governments and farming communities, enabling measures such as preemptive payments to farmers to protect crops from flooding. Similarly, the non-profit TomorrowNow combines AI-driven forecasts with agricultural expertise and delivers tailored advice to farmers via text messages at minimal cost.

The International Federation of Red Cross and Red Crescent Societies also relies on AI-enhanced forecasting to anticipate humanitarian needs, with officials noting unprecedented early warnings about the risks posed by this El Niño. Farmers like Veronica Mukami in Kenya report being better prepared this season thanks to such information, contrasting with previous years when warnings were less timely.

However, the application of AI in forecasting El Niño is met with cautious scrutiny from the scientific community. Experts note that AI models are trained on historical data and may struggle to accurately predict an event unfolding in a climate increasingly altered by global warming. Ko Barrett, deputy secretary-general of the World Meteorological Organization (WMO), points out that while AI performs at least as well as traditional models, reliance on past climate patterns limits future predictive reliability. Liz Stephens, a climate risk professor at Reading University, warns of the danger that unprecedented extremes could arise in places unprepared for such anomalies, as well as new risks appearing in regions previously unaffected.

The WMO expects the sea surface temperature anomaly in a critical Pacific zone, Niño 3.4, to peak at 3.7°C above the long-term average in December, potentially setting a new record since observations began in the 1950s. Effects from the event are projected to extend into 2027, with global air temperatures typically rising following the Pacific sea warming.

Economic impacts are already being felt. Panama, where the El Niño has led to restricted traffic through the vital Panama Canal due to low rainfall, declared a state of emergency in August. The country suffered an estimated $1 billion loss last year linked to diminished maritime commerce and agricultural output. Commodity markets are also adjusting, with hedge funds reportedly leveraging AI tools to predict sugar futures amid weather disruptions, sometimes outperforming industry players caught unprepared.

Nonetheless, the inherent volatility of El Niño and simultaneous climate phenomena such as the Indian Ocean Dipole complicate forecasts. Research from the UK Met Office highlights limitations in some AI models, particularly their inability to anticipate certain extreme weather outcomes accurately, as evidenced by their underperformance in predicting the 2009 post-El Niño winter in northern Europe when compared to physics-based models.

Scott Hosking from the UK’s Alan Turing Institute underscores the importance of AI in addressing the broad and diverse impacts of El Niño worldwide, noting that forecasting thousands of potential scenarios will require AI capabilities. Yet, exactly how AI will best complement traditional approaches remains an open question.

As this historic El Niño event unfolds, the effectiveness of AI-assisted forecasting and its integration with existing meteorological tools will be closely observed, carrying significant implications for preparedness and resilience in a warming world where extreme weather is becoming increasingly frequent.