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Yes—but the evidence points to seasonal risk signals, not a dependable seven-month forecast. A 2026 study in Earth’s Future found that El Niño and tropical Atlantic climate patterns are statistically associated with Amazon hot extremes, with the broadest lagged temperature relationships appearing in March–May (MAM). The paper separately tested predictions of compound hot-and-dry events and found that skill varies substantially by season and region.
What the study found
Hobeichi and co-authors examined whether large-scale ocean–atmosphere patterns precede temperature and rainfall extremes in the Amazon. They found that positive phases of ENSO and tropical Atlantic variability are associated with a greater likelihood of hot extremes. These are statistical relationships, not proof that a particular climate pattern will cause heat in a specific place or month.
The longest and most widespread lagged temperature signal occurs in MAM: relationships with Niño3.4, Tropical North Atlantic (TNA), and Tropical South Atlantic (TSA) index anomalies extend to lags of up to seven months. That number describes statistical dependence between climate indices and hot-temperature extremes. It does not mean the study demonstrated a reliable forecast of a compound hot-and-dry event seven months ahead.
How the signals vary by season
| Season | Temperature relationships reported | Compound-event prediction context |
|---|---|---|
| December–February (DJF) | Recent or concurrent TSA warming is associated with hot extremes across the basin. Longer-lag Niño3.4 relationships occur in some southern and Andes–Amazon foothill regions. | Skill peaks in northern regions in this season. |
| March–May (MAM) | Lagged Niño3.4, TNA, and TSA relationships with hot extremes are widespread and extend to lags of up to seven months. | Skill peaks in the lower central Amazon. |
| June–August (JJA) | The study does not identify a comparably broad temperature precursor signal in its summary of seasonal findings. | Prediction skill is generally weakest. |
| September–November (SON) | Hot extremes are associated mainly with preceding TNA warming; lag patterns differ among climatic subregions. | The study does not report a basin-wide skill peak for this season. |
The table separates observed precursor relationships from the authors’ separate predictive experiments. The study reports seasonal and regional differences rather than a single warning lead time that applies throughout the Amazon.
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Why a precursor is not the same as a forecast
The authors used two different analyses. Copula models assessed tail dependence between climate indices and individual temperature or precipitation extremes at monthly lags. Random Forest experiments assessed how useful lagged indices were for predicting compound hot-and-dry extremes. A relationship in the first analysis does not, by itself, establish useful forecast skill in the second.
For compound events, skill varied by season and region. The authors report that useful predictions generally required at least one climate index at a one-month lead. ENSO was the main contributor to predictability across much of the basin; Atlantic variability also contributed. The North Atlantic Oscillation (NAO) appeared in particular model settings, despite weak direct tail dependence. One exceptionally high regional skill estimate was based on very few events, so it warrants caution.
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Why ocean patterns may matter
The paper describes plausible physical pathways that help explain the statistical relationships. El Niño can weaken the Walker circulation and promote subsidence over northern South America. Less rising air can mean less convection and cloud cover; reduced cloudiness can allow more incoming shortwave radiation, while drying soils can contribute to higher surface temperatures.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →El Niño can also influence tropical Atlantic temperatures after its peak. The authors describe TNA warming as a factor that can help keep the Atlantic Intertropical Convergence Zone farther north, suppressing rainfall over parts of the Amazon and northeastern Brazil. These mechanisms offer context for the observed associations, but they do not turn those associations into a location-specific forecast.
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Rainfall evidence and important limitations
Temperature extremes showed stronger and more widespread relationships with the examined climate indices than precipitation extremes. Dependence involving three-month Standardized Precipitation Index (SPI-3) values was generally weak and spatially sparse. That difference matters: an early temperature signal is not automatically an equally strong signal for rainfall deficits or for compound heat and drought.
The analysis used monthly CRU TS v4.08 gridded temperature and precipitation data at 0.5° by 0.5° resolution, derived from station observations. The authors note that CRU precipitation may be underestimated on the eastern Andean slopes because station coverage is sparse and the terrain is difficult. Monthly gridded data and uneven event counts also limit how precisely results can represent particular locations or rare extremes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this means for warnings
The findings support research into seasonal risk assessment and climate services: large-scale climate indices may help identify periods or regions with elevated heat risk, particularly when interpreted alongside other information. They do not show that an operational public warning system has been deployed or independently validated using this analysis. For readers, the key distinction is between a climate pattern that raises statistical risk and a forecast that can reliably tell communities when and where a compound extreme will occur.
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