Global Climate Models Signal La Niña-Like Impacts Into Early 2026 Despite Shift Toward ENSO-Neutral

WMO-backed forecasts highlight the growing power of multi-model climate intelligence to anticipate heat and rainfall extremes.

New seasonal climate analyses show that global sea-surface temperatures (SSTs) and atmospheric patterns continue to drive La Niña-like impacts into early 2026, even as the Pacific Ocean gradually transitions toward ENSO-neutral conditions, according to the latest outlook from global climate monitoring systems coordinated by the World Meteorological Organization (WMO).

From September–November 2025, SSTs were above average across most of the world’s oceans, with the extratropical North Pacific and North Atlantic particularly warm. The exception was the central and eastern equatorial Pacific, where cooler waters aligned with a weak La Niña, though an intensified east–west temperature gradient sustained stronger-than-expected ocean–atmosphere coupling — most clearly visible in rainfall anomalies.

Ocean Signals Reveal Persistent Climate Forcing

While La Niña conditions were classified as weak based on SST averages alone, climate models detected stronger atmospheric responses, underscoring the importance of gradient-based metrics and coupled modeling systems over single-index classifications.

Key oceanic drivers include:

• A negative Indian Ocean Dipole (IOD), driven by persistent warming in the eastern Indian Ocean

• Above-average SSTs in the North Tropical Atlantic, with near-normal conditions in the south

• Sustained warmth across the extratropical North Atlantic

These patterns highlight how regional SST anomalies increasingly interact across basins, reinforcing compound climate effects.

Early 2026 Outlook: Heat Signals Dominate

For January–March (JFM) 2026, multi-model ensemble forecasts indicate a strong global signal for above-normal land temperatures, with high model agreement across much of the Northern Hemisphere north of 30°N.

Regions with elevated probabilities of above-normal temperatures include:

• Southern and northeastern North America

• Central America and the Caribbean

• The Arctic

• Equatorial Africa and the Maritime Continent

In contrast, weaker or inconsistent signals appear over northwestern North America, parts of Southeast Asia, and southeastern areas of the Indian subcontinent.

Across the oceans, above-normal temperatures are projected over the North Pacific, much of the Atlantic north of the equator, and the eastern Indian Ocean, reinforcing heat-related risks for coastal and marine systems.

Rainfall Outlook: La Niña Influence Persists

Despite the forecast weakening of cold Pacific SST anomalies, rainfall patterns for JFM 2026 remain distinctly La Niña-like due to the sustained east–west SST gradient.

Key rainfall projections include:

• Below-normal rainfall across the central and eastern Pacific

• Enhanced rainfall over the western Pacific and surrounding land regions

• Drier-than-normal conditions over southern North America, parts of eastern Asia, northeastern South America, and the western Indian Ocean

• Increased rainfall probabilities across the Caribbean, northern Europe, northern Asia, northern North America, and the Philippine Sea

These patterns underscore how atmospheric responses can lag or diverge from SST normalization, a critical insight for seasonal forecasting.

Why This Matters for Climate-Tech and AI Forecasting

For climate-tech companies, AI-driven forecasting platforms, agriculture technology firms, energy planners and insurers, the outlook reinforces the growing value of:

• Multi-model ensemble forecasting

• Coupled ocean–atmosphere analytics

• Gradient-based climate indicators

• Region-specific probabilistic risk assessment

As climate variability becomes more nonlinear, decision-ready climate intelligence — rather than single-index signals — is increasingly essential.

Call to Action: Turning Climate Data Into Decisions

Early adopters in climate analytics, AI forecasting, digital twins, and climate-risk platforms are encouraged to integrate these seasonal signals into:

• Energy demand forecasting

• Agricultural planning and food security models

• Water resource management

• Insurance and catastrophe risk analytics

As ENSO variability continues to evolve, the ability to translate probabilistic climate signals into actionable insights will define the next generation of climate resilience tools.