Ahead of COP31 in Antalya, Turkey, concerns are growing regarding global climate risks associated with the projected super El Niño of 2026. Understanding the potential impact of this phenomenon and improving early warning and disaster prevention systems is critically important for enhancing climate resilience. An expert from CGTS invited Zhan Chao, an associate professor at the College of Ecology and Environmental Protection of Beijing Forestry University, to discuss the climatic consequences of super El Niño, the uncertainties in climate forecasting, and ways to transform scientific predictions into proactive disaster prevention.
The 31st session of the Conference of the Parties (COP31) to the UNFCCC will begin on November 9, 2026, in Antalya, Turkey. Meanwhile, abnormal warming continues in the central and eastern equatorial Pacific Ocean.
Recent data from China's National Climate Center indicates that the super El Niño event in the Eastern Pacific officially formed in September 2026 and reached the level of the strongest recorded El Niño by October, with an expected peak in late autumn or early winter.
Since the center of maximum warming is located in the eastern equatorial Pacific, East Pacific El Niño causes more pronounced atmospheric teleconnection effects and usually has a stronger influence on the global climate. Against the backdrop of global warming, warming caused by strong El Niño events tends to significantly raise the average global surface temperature.
According to data from the China Meteorological Administration on global surface temperature, the world's average surface temperature in 2023, 2024, and 2025 exceeded pre-industrial levels (1850-1900) by 1.42, 1.50, and 1.41 degrees Celsius, respectively. Compounded by this super El Niño, studies show a high probability that 2026 will surpass 2024 to become the warmest year in history.
For China, in the East Asian monsoon region, the climatic impact of this powerful signal will affect winter and spring and extend until 2027. The China Meteorological Administration forecasts that temperatures across the country during the upcoming winter will generally be above normal, with slightly more frequent cold waves and sharp temperature fluctuations, demonstrating a 'colder early winter and warmer late winter' pattern.
The total precipitation nationwide is expected to be above average, while northern China may experience frequent intense cold spells and snowfall, and southern China faces a high risk of episodic rain, snow, and frosty weather at low temperatures. In spring and summer of 2027, even though El Niño is expected to weaken rapidly by its end, its lagged impacts on China's climate will remain significant, manifesting as more extreme weather conditions and climatic phenomena, particularly higher temperatures and more intense, widespread summer heatwaves.
It is important to note that El Niño does not generate individual weather events directly; instead, it alters the probability distribution of extreme weather events by exciting atmospheric teleconnection wave patterns, modulating the intensity and position of the subtropical high pressure in the Northwest Pacific, and changing the circulation of the East Asian monsoon.
Due to subsurface ocean temperature anomalies, stochastic atmospheric disturbances (such as western wind bursts), and complex modulation of interdecadal signals under global warming, even super-sized El Niño events can differ in the intensity of tropical atmospheric forcing response and the spatial distribution of their impact.
On the other hand, numerical models have inherent forecasting errors regarding the oceanic forcing signal itself; for example, projections from the North American Multi-Model Ensemble for the peak amplitudes of the 1997 and 2015 super El Niño events showed a systematic overestimation of approximately 0.2 degrees Celsius.
Therefore, scientific climate forecasts are inherently probabilistic forecasts, not deterministic certainties. Furthermore, although El Niño is a powerful forcing signal, it is far from the only driving factor. Climatic anomalies in China are also jointly modulated by numerous boundary conditions and climate regimes, including the Indian Ocean basin regime, the North Atlantic Oscillation, Arctic sea ice, and Eurasian snow cover. Any specific weather event that ultimately materializes is the result of a confluence of multiple factors, meaning that macro-scale climate probabilities cannot be directly equated to the inevitable occurrence of micro-scale individual events.
Statistically warmer or wetter seasonal averages do not preclude the occurrence of localized, episodic extremely cold events or a sudden shift from drought to flood. Ultimately, El Niño shifts the 'odds' for extreme weather, rather than dictating any specific weather event.
Bridging the gap between probabilistic climate forecasts and deterministic disaster prevention actions is both a key agenda item for advancing the Global Goal on Adaptation at COP31 in Antalya and a central priority in implementing China's 15th Five-Year Plan for combating climate change.
To address these forecasting uncertainties, current research focuses on improving coordinated observations in the subsurface equatorial Pacific, in several ocean basins, and in mid-to-high latitudes to enhance model initial conditions, as well as strengthening systematic bias correction for multi-model ensembles and combined dynamic and statistical downscaling to improve prognostic capability and spatial accuracy of regional extremes.
In terms of management and action, the UN is accelerating its 'Early Warning for All' initiative to achieve universal coverage of early warning systems by the end of 2027. Concurrently, China is implementing the provisions of the 15th Five-Year Plan regarding climate risk assessment, early warning, and adaptation, in line with requirements for high-quality meteorological development. By continuing to provide international public goods for early warning through platforms such as Fengyun meteorological satellites, China further strengthens the emergency response communication mechanism led by meteorological early warnings, relying on improved operational coordination across various timeframes to shift disaster management towards pre-disaster preventive measures.
