This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: As of August 2026, the world is bracing for what forecasters describe as one of the strongest El Niño events on record. National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Center has put the chance of a very strong event this fall and winter above 90%, with a 69% likelihood that it will exceed every El Niño since 1950.
This isn't just a "curious weather phenomenon" but a major global shift that we must start preparing for now. In Kenya, for instance, high-risk urban centers have already been flagged where poor drainage and strained infrastructure could turn heavy rainfall into a humanitarian emergency, while coastal counties face the added threat of storm surges and coastal erosion. Kenya's 1997–98 El Niño remains one of the country's most devastating episodes, and subsequent events in 2006–07, 2015–16 and 2023–24 each brought heavy rain, flooding and landslides that killed hundreds and caused extensive damage to infrastructure, agriculture and livelihoods.
This pattern of escalating rainfall extremes is not confined to East Africa or to El Niño events. In September 2023, Storm Daniel brought catastrophic flooding to Derna, Libya, collapsing two dams and killing at least 4,000 people in a single night. The following April, the United Arab Emirates recorded its heaviest rainfall in 75 years, bringing Dubai to a standstill.
That same summer, the Arba'at Dam in eastern Sudan burst under floodwaters, destroying 20 villages and affecting 50,000 people already suffering through a brutal civil war. With a historic El Niño now underway, the urgency of forecasting systems capable of anticipating such extremes before they strike has never been greater. Coincidentally, it is at this moment that artificial intelligence (AI) has emerged as a compelling solution, offering faster, cheaper and more locally tailored weather forecasts without significant infrastructure requirements.
Unlike traditional forecasting systems, which produce a forecast by integrating complex physical equations, AI models learn a direct statistical approximation that links current weather conditions to the future. Learning these approximations requires heavy-duty training on masses of historical weather data. However, once complete, these models are much cheaper to run and well within the reach of ordinary computing hardware.
But when societies are under pressure, taking the easiest solution without fully understanding its limits can be more of a gamble than a rational choice. While machine-learning models like GraphCast can outperform traditional physics-based systems in speed and general accuracy, AI tools come with a critical caveat: The reliability of these systems has not been properly vetted for extremes, specifically under conditions of a changing climate. This challenge is known as the "extrapolation problem." While AI excels at identifying patterns in historical data, it can struggle to predict "unprecedented" events: the record-breaking heat waves, floods and storms that fall outside its training set.
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