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April 2026 Quick Look

Published: April 20, 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)

As of mid-April 2026, the equatorial Pacific is in an ENSO-neutral state but rapidly transitioning toward El Niño. The latest CCSR/IRI ENSO plume forecast gives a 70% chance of El Niño developing in April–June 2026 versus 30% for continued neutrality, with El Niño remaining dominant through the rest of 2026 at high probabilities of 88–94%.

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: April 20, 2026

Note: The SST anomalies cited below refer to the OISSTv2 SST data set, and not ERSSTv5. OISSTv2 is often used for real-time analysis and model initialization, while ERSSTv5 is used for retrospective official ENSO diagnosis because it is more homogeneous over time, allowing for more accurate comparisons among ENSO events that are years apart. These two products may differ, particularly during ENSO events. The difference between the two datasets may be as much as 0.5 °C. Additionally in some years, the ERSSTv5 may tend to be cooler than OISSTv2 in the context of warming trends, because ERSSTv5 is expressed relative to a base period that is updated every 5 years, while the base period of OISSTv2 is updated every 10 years. In February 2021, both datasets were updated to reflect the 1991-2020 climatology period.

Recent and Current Conditions

The observed SST anomaly in the NINO3.4 region during the Jan–Mar 2026 season was -0.25 °C, and for March 2026, it was +0.03 °C. The most recent weekly average (week centered on Apr 15, 2026) of the NINO3.4 index was +0.5 °C. These values indicate that Pacific sea-surface temperatures are in an ENSO-neutral state and are beginning to develop into El Niño. The CCSR/IRI’s definition of El Niño, requires that the monthly SST anomaly in the NINO3.4 region (5°S-5°N; 170°W-120°W) exceed +0.5 °C. Similarly, for La Niña, the anomaly must be -0.5 °C or colder.

In March 2026, the Southern Oscillation Index (SOI) stood at +8.9, while the equatorial SOI hovered near neutral at -0.1, highlighting a sharp contrast between the two measures. Low-level (850-hPa) wind anomalies were westerly over the western and central equatorial Pacific and weak easterly over the eastern Pacific. Upper-level (200-hPa) wind anomalies were westerly over the east-central and eastern Pacific. Below-average OLR (enhanced convection and precipitation) was evident east of Papua New Guinea. Above-average OLR (suppressed convection and precipitation) was observed over parts of Indonesia, Southeast Asia, the Philippines, and east of the Date Line along the equatorial Pacific. Above-average subsurface temperatures have strengthened and spread across the Pacific, reaching the far eastern basin. Warmer waters now dominate much of the subsurface, and their eastward expansion has pushed the Niño 1+2 index up to +1.8 °C (week centered on 15 April 2026), with Niño 3 at +0.6 °C and Niño 4 at +0.9 °C. Together, these signals point to rapidly evolving ENSO conditions favoring a swift onset of El Niño.

Expected Conditions

Note – Only models that produce a new ENSO prediction every month are considered in this statement.

The El Niño/Southern Oscillation (ENSO) Diagnostic Discussion, released on 09 April 2026 by the Climate Prediction Center (CPC)/NCEP/NWS, the “Final La Niña Advisory” was issued alongside an “El Niño Watch,” with ENSO-neutral conditions favored through April–June 2026 (80% chance) before El Niño is likely to emerge in May–July 2026 (61% chance) and persist through at least the end of 2026.

The latest set of ENSO prediction models from mid-April 2026 is now available in the CCSR/ IRI ENSO prediction plume. These are used to assess the probabilities of the three ENSO categories by using the average value of the NINO3.4 SST anomaly predictions from all models in the plume, equally weighted. A standard Gaussian error is imposed over that averaged forecast, with its width determined by an estimate of overall expected model skill for the season of the year and the lead time. Higher skill results in a relatively narrower error distribution, while low skill results in an error distribution with width approaching that of the historical observed distribution.

According to the April 2026 CCSR/IRI ENSO plume forecast, El Niño conditions dominate April–June (AMJ) 2026 with a 70% probability and remain strongly favored through the forecast period (88–94%). ENSO-neutral chances drop from 30% in AMJ to 12% in May–July and stay low thereafter, while La Niña redevelopment is negligible. Caution is advised when interpreting these longer-lead ENSO forecasts, as they extend through the boreal spring predictability barrier; accordingly, long-range outlooks should be viewed with appropriate uncertainty, even if they suggest a possible emerging outcome. A plot of the probabilities, shown below, summarizes the forecast evolution. The climatological probabilities for La Niña, ENSO-neutral, and El Niño conditions vary seasonally, and are shown by the lines on the plot, and are given in a table at the bottom of this page for each 3-month season.

Caution is also advised in interpreting the forecast distribution from the Gaussian standard error as the actual probabilities, due to differing biases and performance of the different models. In particular, this approach considers only the mean of the predictions, and not the total range across the models, nor the ensemble range within individual models. At longer leads, the skill of the models degrades, and uncertainty in skill must be convolved with the uncertainties from initial conditions and differing model physics, which leads to more climatological probabilities in the long-lead ENSO Outlook than might be suggested by the suite of models. Furthermore, the expected skill of one model versus another has not been established using uniform validation procedures, which may cause a difference in the true probability distribution.

IRI ENSO Forecast Histogram Image
Season La Niña Neutral El Niño
AMJ 0 30 70
MJJ 0 12 88
JJA 0 8 92
JAS 0 6 94
ASO 0 8 92
SON 0 8 92
OND 1 9 90
NDJ 1 11 88
DJF 1 11 88

ENSO Forecast

IRI ENSO Predictions Plume

Published: April 20, 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

Forecast SST Anomalies (deg C) in the Nino 3.4 Region

Seasons (2026 – 2027)
Model AMJ MJJ JJA JAS ASO SON OND NDJ DJF
Dynamical Models
AUS-ACCESS 1.40 1.83 2.20 2.57
AUS-RELATIVE 1.00 1.37 1.67 2.03
BCC DIAP 0.40 0.76 1.05 1.32 1.55 1.72 1.89 1.98 1.91
CMC CANSIP 0.86 1.18 1.35 1.38 1.38 1.44 1.53 1.60 1.60
COLA CCSM4 1.28 1.72 2.11 2.40 2.53 2.60 2.57 2.45 2.26
CS-IRI-MM 0.56 0.83 1.10 1.40 1.75 2.12
GFDL SPEAR 0.79 1.21 1.58 1.86 2.07 2.35 2.68 2.92 2.93
IOCAS ICM 0.30 0.44 0.67 0.80 0.86 0.88 0.90 0.94 0.95
JMA 0.53 0.82 1.10 1.36 1.66
KMA 1.12 1.58 2.01 2.37
LDEO 0.30 0.41 0.42 0.41 0.52 0.73 0.91 1.03 1.00
MetFRANCE 0.61 0.96 1.27 1.55 1.79
NASA GMAO 0.92 1.49 2.13 2.75 3.23 3.54 3.73
NCEP CFSv2 0.69 1.15 1.51 1.78 1.99 2.22 2.31
SINTEX-F 0.66 1.06 1.41 1.68 1.81 1.91 2.02 2.07 2.03
UKMO 0.80 1.13 1.43 1.70
Average, Dynamical models 0.764 1.120 1.438 1.709 1.763 1.951 2.060 1.856 1.810
Statistical Models
BCC_RZDM 0.41 0.53 0.63 0.69 0.81 0.94 1.06 1.07 0.99
CPC MRKOV 0.07 0.17 0.24 0.34 0.47 0.62 0.76 0.90 0.99
CSU CLIPR 0.21 0.33 0.44 0.56 0.73 0.91 1.08 0.99 0.91
IAP-NN 0.30 0.48 0.64 0.78 0.87 0.93 0.96 0.98 0.98
NTU CODA 0.39 0.56 0.75 0.89 0.99 1.17 1.25 1.24 1.16
TONGJI-ML -0.11 0.20 0.52 0.69 0.63 0.46 0.32 0.44 0.64
UCLA-TCD 0.89 1.20 1.45 1.63 1.77 1.85 1.87 1.79 1.62
UW PSL-CSLIM 0.78 1.12 1.33 1.49 1.65 1.82 1.95 1.97 1.84
UW PSL-LIM 0.73 1.10 1.41 1.62 1.73 1.75 1.70 1.61 1.48
XRO 0.29 0.51 0.74 0.97 1.22 1.51 1.79 1.99 2.05
Average, Statistical models 0.396 0.619 0.816 0.966 1.088 1.195 1.274 1.298 1.265
Average, All models 0.622 0.928 1.199 1.423 1.456 1.573 1.647 1.528 1.489

Discussion of Current Forecasts

The multimodel mean of statistical and dynamical models indicates El Niño onset in April–June 2026 with a 70% probability, compared to a 30% chance of continued ENSO-neutral conditions. From May–June 2026 through December–February 2026/27, El Niño remains the dominant category, with consistently high probabilities ranging from 88% to 94%, indicating that the odds are firmly in favor of El Niño prevailing for the remainder of 2026. Note that long-lead ENSO forecasts are highly uncertain, especially when crossing the spring predictability barrier. Based on the multi-model mean (Dynamical and Statistical models) prediction, and the expected skill of the models by start time and lead time, the probabilities (in %) for La Niña, ENSO-neutral and El Niño conditions (using -0.5 °C and 0.5 °C thresholds) over the coming 9 seasons are:

Season La Niña Neutral El Niño
AMJ 0 30 70
MJJ 0 12 88
JJA 0 8 92
JAS 0 6 94
ASO 0 8 92
SON 0 8 92
OND 1 9 90
NDJ 1 11 88
DJF 1 11 88

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: April 20, 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: April 20, 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 March 2026, the observed Dipole Mode Index was +0.15, 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, GFDL, and NASA along with their equally weighted multi-model mean (MME). Observations show that the DMI (shown in black) rose steadily from its negative-phase peak in October 2025, gradually weakening through late 2025 and returning to neutral IOD conditions by February 2026. Neutral conditions persist from January through March 2026. Forecasts indicate that the IOD will remain neutral during April and May 2026, with a gradual tendency toward positive IOD conditions. By June 2026, four out of six models exceed the positive IOD threshold and remain in positive territory through the end of the forecast period in October 2026. The multi-model ensemble also indicates a shift to positive IOD conditions from July 2026 onward, persisting through August, September, and October 2026.

Probabilistic IOD Forecasts from the NMME

Based on April 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 (92 in this case), 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 April and May 2026, neutral IOD conditions dominate, while the likelihood of positive IOD increases from ~1% in April to ~23% in May. From June through October, the probability of positive IOD continues to rise, while neutral conditions gradually decline. Probabilities of the negative IOD phase remain low throughout the forecast period. This transition from neutral toward positive IOD is broadly consistent with the CCSR/IRI ENSO outlook, which indicates a persistent dominance of El Niño conditions in the April 2026 forecasts.

In summary, April and May 2026 favor neutral IOD conditions, with a rising probability of positive IOD (from ~1% to ~23%), which then continues to strengthen and becomes dominant for the remainder of the forecast period.

April 20, 2026 IOD Model Based Forecast
Historical SST Anomalies Image

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.

The IRI ENSO forecast is released on the 19th of each month. If the 19th falls on a weekend or holiday, it is released on the closest business day.

Forecast and model data used in our probabilistic forecast can be accessed by submitting a Request to Access IRI ENSO Data.

All data from this website is covered under the Creative Commons Attribution 4.0 License. When citing IRI ENSO images or data, please use "Images [or Data] provided by The International Research Institute for Climate and Society, Columbia University Climate School", with a link to https://iri.columbia.edu/ENSO.