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June 2026 Quick Look

Published: June 22, 2026

A monthly summary of the status of El Niño, La Niña, and the Southern Oscillation, or ENSO, based on the NINO3.4 index (120-170W, 5S-5N)

El Niño conditions are strengthening across the tropical Pacific, with SST anomalies in the Niño 3.4 region showing a steady upward trend. The observed SST anomaly reached +0.48 °C during March–May 2026 and increased to +0.94 °C in May 2026. The latest weekly Niño 3.4 index, centered on June 17, 2026, climbed further to +1.7 °C. Together, these observations indicate that Pacific Ocean conditions have transitioned into El Niño conditions and are continuing to intensify toward a moderate-strength El Niño event.

Figures 1 ((the official CPC ENSO probability forecast) and 3 (the objective model-based IRI ENSO probability forecast) are often quite similar. However, occasionally they may differ noticeably. There can be several possible reasons for differences. One is the human forecasters, using their experience and judgment, may disagree to some degree with the models, which may have known biases. Another reason is the models are not run at the same time that the forecasters make their assessment, so that the starting ENSO conditions may be slightly different between the two times. The charts on this Quick Look page are updated at two different times of the month, typically about a week apart, with the IRI forecast run later. Also note that the CPC forecast starts on the previous season while the IRI forecast starts on the current season.
Click on the for more information on each figure.

Historically Speaking

    El Niño and La Niña events tend to develop during the period Apr-Jun and they
  • Tend to reach their maximum strength during October - February
  • Typically persist for 9-12 months, though occasionally persisting for up to 2 years
  • Typically recur every 2 to 7 years

IRI ENSO Forecast

IRI Technical ENSO Update and Model-Based Probabilistic ENSO Forecast

Published: June 22, 2026

ENSO Current State: Ocean–Atmosphere Overview

El Niño conditions are strengthening across the tropical Pacific, with SST anomalies in the Niño 3.4 region showing a steady upward trend. The observed SST anomaly reached +0.48 °C during March–May 2026 and increased to +0.94 °C in May 2026. The latest weekly Niño 3.4 index, centered on June 17, 2026, climbed further to +1.7 °C. Together, these observations indicate that Pacific Ocean conditions have transitioned into El Niño conditions and are continuing to intensify toward a moderate-strength El Niño event.

Both oceanic and atmospheric indicators are becoming increasingly aligned, supporting further intensification of the ongoing El Niño event. On the atmospheric side, the Southern Oscillation Index (SOI) declined to -14.5 in May 2026, while the latest 30-day SOI value through June 20 remained strongly negative at -21.9. These persistently negative SOI values reflect a further weakening of the Walker circulation, consistent with continued El Niño development and intensification. In addition, the Equatorial SOI declined to -1.0 during May 2026, further indicating that the atmosphere is starting to respond to the warming Pacific Ocean, a sign that ocean-atmosphere coupling associated with El Niño is becoming established. Low-level (850-hPa) westerly wind anomalies were observed across the western and east-central equatorial Pacific, indicating a weakening of the trade winds and supporting further El Niño intensification. Further, the subsurface temperature structure in the central–eastern equatorial Pacific exhibits a pronounced warming signal. Between approximately 150°W and 80°W, temperatures at depths of 50–150 m have increased substantially, with anomalies locally reaching up to 6 °C. This substantial subsurface heat reservoir could serve as an important energy source for the continued development and intensification of El Niño conditions, provided that favorable atmospheric coupling is maintained. In addition, ocean heat content anomalies (averaged over the upper 0–300 m) between the Date Line and 80°W are also markedly elevated. For context, current values are almost twofold than those observed during the same period in mid-June 2023, during the development phase of the 2023 El Niño event.

ENSO Probabilities

To generate the ENSO probability outlook, forecasts from all 24 participating models (15 dynamical and 9 statistical) are combined into an equally weighted multi-model average of Niño3.4 SST anomalies. A Gaussian error distribution is then applied to the ensemble-mean forecast, with its width determined by the expected forecast skill for the season and lead time. Higher forecast skill results in a narrower distribution and greater confidence, while lower skill produces a broader range of possible outcomes. Probabilities are calculated based on the likelihood of Niño3.4 SST anomalies falling within the standard ENSO thresholds: El Niño (≥ +0.5°C), ENSO-neutral (-0.5°C to +0.5°C), and La Niña (≤ -0.5°C). This methodology translates the multi-model forecast into probabilities for each ENSO category.

The June 2026 outlook strongly favors the persistence of El Niño conditions throughout the forecast period. El Niño probabilities are assigned at 100% from JJA through SON. From OND to DJF, the probabilities remain exceptionally high at 99%, followed by 98% and 97% for JFM and FMA, 2027 respectively, with the remaining probabilities assigned to ENSO-neutral conditions. No probability is assigned to La Niña development during this period. June marks the end of the boreal spring predictability barrier; therefore, the high-confidence outlook is consistent with strong model consensus and indicates a high likelihood of El Niño persisting into early 2027. The accompanying probability plot summarizes the forecast evolution and the changing likelihood of each ENSO phase throughout the forecast period.

ENSO Strength

Forecasts of ENSO strength are estimated through a consensus-based model counting approach using a set of Niño 3.4 sea surface temperature anomaly thresholds, to indicate weak, moderate, strong and very strong El Nino and La Nina categories.

The mid-June 2026 CCSR/IRI model ensemble strongly favors a developing and intensifying El Niño event, with all models indicating El Niño conditions throughout the forecast period. The ensemble projects the highest intensity during SON 2026, when 13 out of 24 models indicate a very strong El Niño event (Niño 3.4 ≥ +2.0 °C), representing the peak of the forecasted event strength. Overall, the ensemble shows a strong and consistent signal for a continuing and intensifying El Niño event, with negligible likelihood of ENSO-neutral or La Niña conditions.

Notes:

The SST anomalies cited below are based on the NOAA Optimum Interpolation Sea Surface Temperature (OISSTv2)dataset. The climatology period is 1991–2020.

The primary metric used to monitor the El Niño–Southern Oscillation (ENSO) is the traditional Niño3.4 index (TONI), defined as the area-averaged sea surface temperature anomaly over the Niño3.4 region (5°S–5°N, 170°W–120°W).

According to the CCSR/IRI definition, El Niño conditions occur when monthly TONI exceeds +0.5 °C, while La Niña conditions occur when monthly TONI falls below −0.5 °C.

An ENSO “event” is considered established when the TONI threshold (±0.5 °C) persists for at least five consecutive overlapping 3-month seasons (e.g., SON, OND, NDJ, DJF, JFM).

The ENSO Intensity Classification used (based on TONI) is;

El Niño Strength La Niña Strength
Weak: +0.5 to +1.0 °C Weak: −0.5 to −1.0 °C
Moderate: +1.0 to +1.5 °C Moderate: −1.0 to −1.5 °C
Strong: +1.5 to +2.0 °C Strong: −1.5 to −2.0 °C
Very Strong: > +2.0 °C Very Strong: < −2.0 °C

IRI ENSO Forecast Histogram Image

ENSO Forecast

IRI ENSO Predictions Plume

Published: June 22, 2026

Note on interpreting model forecasts

The following graph and table show forecasts made by dynamical and statistical models for SST in the Nino 3.4 region for nine overlapping 3-month periods. Note that the expected skills of the models, based on historical performance, are not equal to one another. The skills also generally decrease as the lead time increases. Thirdly, forecasts made at some times of the year generally have higher skill than forecasts made at other times of the year--namely, they are better when made between June and December than when they are made between February and May. Differences among the forecasts of the models reflect both differences in model design, and actual uncertainty in the forecast of the possible future SST scenario.

Interactive Chart

You can highlight a specific model by hovering over it either on the chart or the legend. Selecting an item on the legend will toggle the visibility of the model on the page. You can also select DYN MODELS or STAT MODELS to toggle them all at once. Clicking on the "burger" menu above the legend will give you options to download the image or expand to full screen. If you have any feedback on this new feature, please let us know at webmaster@iri.columbia.edu.

List of Models Used

Discussion of Current Forecasts

ENSO Prediction Plume: Model-by-Model Outlook (Traditional Nino3.4 index)

The mid-June 2026 CCSR/IRI ENSO prediction plume has been updated with the latest model forecasts. The outlook shows strong agreement among the 24 participating models (15 dynamical and 9 statistical) that El Niño conditions will strengthen further during 2026 and persist into early 2027. All models maintain positive Niño3.4 anomalies throughout the forecast period, indicating a high level of confidence in the ongoing warming of the central and eastern tropical Pacific. Most dynamical models forecast Niño3.4 anomalies exceeding +2.0°C by Jul-Sep. Statistical models indicate a somewhat weaker but still substantial warming signal. While differences remain in the projected peak intensity, the overall message is consistent across the forecast suite: a strong El Niño event is likely to develop and remain in place through late 2026, with potentially severe climate impacts extending into 2027.

Summary of forecasts issued over last 22 months

The following interactive plot shows the model forecasts issued not only from the current month (as in the plot above), but also from the 21 months previous to this month. The observations are shown up to the most recently completed 3-month period. The plots allow comparison of plumes from the previous start times, or examination of the forecast behavior of a given model over time.
Hovering over any single model will highlight that particular model in the chart.
Clicking a particular model will hide/show that model in the chart.
At the bottom of the plot, you can select which models to show in the chart: all the models, the dynamical models only, or the statistical models only.

Notes on the data 

Only models producing forecasts on a monthly basis are included. This means that some models whose forecasts appear in the Experimental Long-Lead Forecast Bulletin (produced by COLA) do not appear in the table.

Once an IRI ENSO probability forecast has been published, the results stand even if a model reports an error and changes their data. When this happens we will update the plume with the model's correct values even though our forecast hasn't changed. What this means is that our forecast is always the same, but the underlying data may be different from what we based our forecast on.

The SST anomaly forecasts are for the 3-month periods shown, and are for the Nino 3.4 region (120-170W, 5N-5S). Often, the anomalies are provided directly in a graph or a table by the respective forecasting centers for the Nino 3.4 region. In some cases, however, they are given for 1-month periods, for 3-month periods that skip some of the periods in the above table, and/or only for a region (or regions) other than Nino 3.4. In these cases, the following means are used to obtain the needed anomalies for the table:

  • Temporal averaging
  • Linear temporal interpolation
  • Visual averaging of values on a contoured map

The anomalies shown are those with respect to the base period used to define the normals, which vary among the groups producing model forecasts. They have not been adjusted to anomalies with respect to a common base period. Discrepancies among the climatological SST resulting from differing base periods may be as high as a quarter of a degree C in the worst cases. Forecasters are encouraged to use the standard 1991-2020 period as the base period, or a period not very different from it.

ENSO Forecast

Forecast Probability Distribution Based on the IRI ENSO Prediction Plume

Published: June 22, 2026

The plots on this page show predictions of seasonal (3-month average) sea surface temperature (SST) anomaly in the Niño3.4 region in the east-central tropical Pacific (5°N-5°S, 120°-170°W), covering the nine overlapping seasons beginning with the current month. The predictions are based on the large (20+) set of dynamical and statistical models in the plume of model ENSO predictions.

  • Model Based Prediction Percentiles Image

    Figure 5

    Predictions of ENSO are probabilistic. The ensemble mean prediction is only a best single guess. On either side of that prediction, there is a substantial uncertainty distribution, or error tolerance. The second plot (Figure 2) shows the estimated probability distribution of the predictions, showing a set of percentiles within that distribution for each lead time. The distribution is modeled as a normal (Gaussian) distribution, so that the overall mean forecast represents the center, or 50 percentile, in the distribution. The overall mean is formed using equal weighting among all models. On either side, other percentile values are shown symmetrically, ranging from 1 to 99 and including some intermediate percentiles (5 and 95, 15 and 85, and 25 and 75). The plot enables a user to estimate the probability of the Niño3.4 SST anomaly to be greater or less than some critical value, or within some interval. If, for example, the 85 percentile falls at 1.8° C above average, the probability of the SST exceeding 1.8° C can be estimated at 15%. Probabilities for exceeding or not exceeding values not exactly on percentile line can be roughly interpolated by eye. The overall width of the probability distribution is derived from the historical skill of the hindcasts of the models, from 1982 to present, for the specific forecast start time and lead time. This method of defining the probability distribution represents one of two general approaches, the other approach being a direct counting of ensemble members within each of the percentile bands. This second approach assumes that the ensemble spreads of the models are true representations of the uncertainty. Individual model spreads have often been found to be somwehate narrower than they should be, although in multi-model ensembles this tendency has been shown to be milder or even eliminated.

  • Model Based Prediction Distribution Image

    Figure 6

    Figure 6, sometimes called a spaghetti diagram, shows synthetically generated prediction scenarios that are equally likely. Here, 100 scenarios are shown; any number can be generated for such a diagram. Each scenario is produced using a random number generator, combined with knowledge of the mean forecast and its uncertainty, as well as the amount of persistence of anomalies. The degree of persistence of anomalies is based on the correlation of prediction errors from one lead time to another. In other words, the individual lines are designed to show the correct amount of persistence as expected in nature, rather than jumping around more randomly from one lead time to the next. The uncertainty and persistence statistics are based on the set of 7 NMME (North American Multimodel Ensemble) models, as it is assumed that these statistics are approximately applicable to all of the models. Sometimes the “spaghetti density” may appear asymmetric about the mean of all the forecasts or outside of the 85 and 15 percentile lines. This is purely sampling variability, and would not occur if many thousands of such lines were plotted. But with that many lines, most of the plot would be too crowded to get a sense of the behavior of the lines near the center of the distribution. The main purpose of the diagram is to serve users who want to assess realistic individual scenarios of ENSO behavior rather than statistical summaries of the forecast like the percentiles shown in the second plot.

IOD Forecast

Published: June 22, 2026

Note: The Dipole Mode Index is calculated based on the ERSSTv5 data. To account for evolving background conditions and long-term warming, SST anomalies were calculated relative to a sliding monthly climatology. For each month in the time series, the climatology was computed as the mean SST for that calendar month over the prior 30 years. The Dipole Mode Index (DMI) is then defined as the difference in sea surface temperature anomalies between the western equatorial Indian Ocean (50°E–70°E, 10°S–10°N) and the southeastern equatorial Indian Ocean (90°E–110°E, 10°S–0°), and is used to quantify the strength and phase of the Indian Ocean Dipole (IOD).

Current Conditions

In May 2026, the observed Dipole Mode Index was -0.27 °C, indicating neutral IOD conditions across the Indian Ocean. According to the CCSR/IRI’s criteria for defining positive and negative Indian Ocean Dipole (IOD) events, a positive IOD phase is defined when the DMI exceeds +0.4 °C, and a negative IOD when it falls below −0.4 °C. The IOD is considered inactive (neutral) when the DMI lies between −0.4 °C and +0.4 °C.

Model-Based IOD Outlook: Deterministic Forecasts from the NMME

Forecasts from the latest set of operational models in the North American Multi-Model Ensemble (NMME) project are used to construct deterministic IOD forecasts from each individual model using its ensemble mean DMI to form an IOD forecast plume. The IOD plume plot shows the latest set of predictions from CESM1, CFSv2, CanESM5, GEM-NEMO, and NASA along with their equally weighted multi-model mean (MME). Observations show that the DMI (black line) has remained in a neutral state since January 2026, briefly turning negative in May 2026, as reflected in the observed DMI value for that month. Forecasts indicate that the IOD will remain neutral during June 2026, with a gradual tendency toward positive IOD conditions. By July 2026, three out of five models exceed the positive IOD threshold and remain in positive territory through the end of the forecast period in December 2026. The multi-model ensemble also indicates a shift toward positive IOD conditions from July 2026 onward, persisting through the end of the forecast period.

Probabilistic IOD Forecasts from the NMME

Based on June 2026 initialization data, the model-based probabilistic forecast of the Indian Ocean Dipole was generated by CCSR/IRI to assess potential phase developments. Probabilities are computed using an ensemble-member counting method, where all ensemble members from the contributing models combined (62 in all), are pooled to determine the likelihood of a negative, neutral, or positive IOD phase for the upcoming months. Climatological probabilities for each IOD phase, based on historical data, are shown as dotted lines for reference. In June 2026, neutral IOD conditions dominate, while the likelihood of positive IOD increases from ~2% in June to ~61% in July. From August through December, the probability of positive IOD continues to rise to over 90% by September, while neutral conditions decline to less than 10%. Probabilities of the negative IOD phase remain negligible throughout the forecast period. This transition from neutral toward positive IOD is broadly consistent with the CCSR/IRI ENSO outlook, which indicates a persistently high dominance of El Niño conditions in the June 2026 forecasts.

In summary, June 2026 favor neutral IOD conditions, with a rising probability of positive IOD (from ~2% to ~97%), which then continues to strengthen and becomes highly dominant for the remainder of the forecast period.

Figure: IOD Plume and Probabilities (June, 2026).

June 22, 2026 IOD Model Based Forecast
Historical SST Anomalies Image

Contact Us

Please contact us for opportunities to collaborate on research using the IRI ENSO Forecasts. We are always interested in collaborating with researchers and organizations to advance our understanding of ENSO and its impacts. Please contact us at products@iri.columbia.edu or fill out this form, and we can get back to you regarding your interests:

References

Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events. Ehsan, M.A., L’Heureux, M.L., Tippett, M.K., Robertson, A.W, Turmelle, J.P., npj Clim Atmos Sci, 2024.

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