What Does IMD's Probabilistic Monsoon Forecast Really Mean?

Every year, when the India Meteorological Department (IMD) releases its seasonal monsoon forecast, one common question immediately follows: “So, will the monsoon definitely be good or bad this year?” This year, many students, friends, weather enthusiasts, and members of the public asked similar questions after seeing IMD’s probabilistic monsoon forecast map. At first glance, the map may look confusing because it uses probabilities instead of giving a simple “yes” or “no” answer. However, this probabilistic approach is one of the most scientifically meaningful ways to describe seasonal monsoon forecasts. Unlike a daily weather forecast, which predicts conditions over the next few days, a seasonal monsoon forecast describes the likelihood of different rainfall outcomes several months in advance. Understanding this distinction is the key to interpreting the forecast correctly.

Figure 1. Probabilistic forecast of rainfall categories (Below Normal, Normal, and Above Normal) over India during the 2026 southwest monsoon season (June–September) issued by the India Meteorological Department (IMD). The colours indicate the rainfall category with the highest probability at each location. It is important to note that the map does not show the expected amount of rainfall. Instead, it shows which rainfall category (Below Normal, Normal, or Above Normal) is currently considered the most likely at a given location. [This image is regenerated for Educational purpose].

The IMD forecast map shows the probability of three possible rainfall categories during the monsoon season:

• Below Normal rainfall
• Normal rainfall
• Above Normal rainfall

Different colours indicate which category currently appears more likely over different parts of India. For example:

• Yellow–orange shades indicate a relatively higher probability of below-normal rainfall
• Green shades indicate a higher probability of normal rainfall
• Blue shades indicate a higher probability of above-normal rainfall

But here comes the most important point: A higher probability does NOT mean that category will definitely occur. It simply means that among several possible outcomes, that category currently appears more likely based on scientific analysis and climate model guidance.

Understanding the Probabilistic Forecast Through Cricket

Let us use a cricket example. Suppose India is playing Australia in a major cricket final. Before the match, cricket experts may say: “India has a 65% chance of winning.” Why?

Because experts may look at:

• recent team form,
• pitch conditions,
• weather,
• player performance,
• and past statistics.

Does this guarantee India will win? No. Australia can still win the match. The prediction only means:

• India appears more likely to win,
• but uncertainty still exists.

Monsoon forecasts work in the same way.

Figure 2. A cricket analogy to explain probabilistic forecasting. A 65% chance of India winning does not guarantee victory; it simply indicates that India is more likely to win based on the available information. Higher probability means a more likely outcome, not a guaranteed result. Monsoon forecasts are interpreted in the same way.

Understanding the Percentages in Probabilistic Forecast

An important point that is often misunderstood is that even if one category has the highest probability, the other categories remain possible. For example, suppose a region shows:

• 50% probability of Below Normal rainfall
• 30% probability of Normal rainfall
• 20% probability of Above Normal rainfall

This does not mean below-normal rainfall is guaranteed. It simply means:

• Below-normal rainfall is currently the most likely outcome,
• but there is still a combined 50% chance that rainfall may become Normal or even Above Normal.

Therefore, the category with the highest probability is not guaranteed to occur; it is simply more likely than the other available categories.

Figure 3. Understanding probabilistic monsoon forecasts. Even when one rainfall category has the highest probability, other outcomes remain possible. In this example, below-normal rainfall (50%) is the most likely outcome, but there is still a combined 50% chance that rainfall could be normal (30%) or above normal (20%). Thus, a higher probability indicates a more likely scenario, not a guaranteed outcome.

How Is IMD’s Probabilistic Forecast Prepared?

A common question is: “How does IMD prepare this probabilistic forecast?” The forecast is not based on a single climate model. Instead, IMD uses information from multiple climate models and many model simulations from several national and international forecasting centres. By combining information from many independent forecasting systems, scientists can obtain a more robust estimate of future monsoon conditions.

Think of it like asking many experienced cricket experts to predict the outcome of an important match. One expert may focus on:

• recent team performance,
• another on pitch conditions,
• another on weather,
• and another on player statistics.

Each may provide a slightly different prediction. Rather than depending on one opinion, combining information from many experts generally provides a better estimate. Similarly, scientists combine multiple climate models and many simulations using a multi-model ensemble approach. Combining information from many models helps reduce uncertainty and improve forecast skill.

Figure 4. Schematic illustration of how IMD prepares its probabilistic seasonal monsoon forecast. Climate predictions from multiple national and international climate models, together with numerous model simulations (ensembles), are combined using statistical techniques to estimate the most likely rainfall categories and their probabilities. The multi-model ensemble approach helps reduce uncertainty and improve forecast skill.

Why Are Forecasts Probabilistic?

To understand why seasonal forecasts are expressed as probabilities, we need to bring together all the pieces discussed so far. Now, let us connect everything together.

• the Indian monsoon is influenced by many interacting factors such as El Niño, the Indian Ocean Dipole (IOD), sea-surface temperatures, atmospheric circulation, and weather systems,
• these factors continuously evolve throughout the season,
• and IMD combines information from multiple climate models and hundreds of simulations to prepare its forecast.

But even after using the best available observations, scientific knowledge, and climate models, it is impossible to predict the exact behaviour of the atmosphere several months in advance with complete certainty. This is because nature is inherently complex. Different climate models may produce slightly different outcomes. Even different simulations from the same model may show some variation because small differences in the initial conditions can grow over time.

For example, one set of simulations may suggest a higher chance of below-normal rainfall, while others may indicate normal rainfall. By analysing all these simulations together, scientists can estimate how likely each outcome is. This is why seasonal forecasts, including those from IMD, are expressed as probabilities rather than as definite predictions. In simple words, Probabilistic forecasts tell us what is most likely to happen, while acknowledging that other outcomes remain possible.

Why Are Probabilistic Forecasts Useful?

Some people may ask: “If forecasts are uncertain, why use them?”. Uncertainty does not make forecasts useless. In fact, probabilistic forecasts are extremely useful for:

• agriculture planning,
• reservoir management,
• drought preparedness,
• flood preparedness,
• and policy decisions.

Just like cricket teams prepare strategies based on likely pitch and weather conditions before a match, governments and farmers use monsoon forecasts to prepare for likely seasonal scenarios. The forecast gives early guidance — not a guaranteed script of the season.

Summary

Perhaps the most important takeaway is this: seasonal monsoon forecasts are scientific estimates of likelihood. In simple words, a higher probability means a more likely outcome, not a guaranteed one. Understanding this distinction helps us interpret forecasts realistically and appreciate both the power and the limitations of science. The monsoon always contains some uncertainty—and that is part of what makes it both challenging and fascinating. A probabilistic forecast is therefore not a promise of what will happen, but a scientific assessment of what is more likely to happen based on the best available observations, climate models, and scientific understanding. By interpreting probabilities correctly, we can make better decisions, manage risks more effectively, and be better prepared for the range of possible monsoon outcomes.

References:

IMD's latest seasonal monsoon forecast was released on 29th May 2026. https://internal.imd.gov.in/press_release/20260529_pr_5028.pdf

https://imdpune.gov.in/prediction.php

https://www.satyabanbratna.com/publications

Disclaimer: The views and interpretations presented in this blog are solely those of the author and are intended for educational and science communication purposes. They do not necessarily represent the official views, policies, forecasts, or positions of the India Meteorological Department (IMD) or any other organization with which the author is affiliated.