India’s seasonal monsoon forecasts estimate broad rainfall patterns and seasonal totals; they do not predict the exact date and place of every rainstorm. Forecasters combine statistical relationships with simulations from coupled ocean–atmosphere models, then update the outlook as the season approaches and unfolds. Those methods can inform planning, but uncertainty remains—especially for rainfall in a particular place or week.
What a long-range monsoon forecast actually predicts
This article focuses on the India Meteorological Department’s (IMD) forecasts for India’s June–September southwest monsoon. Other monsoon systems around the world have their own forecasting agencies, regions and methods.
A seasonal outlook describes averaged weather over a broad region and time period. The IMD Pune Climate Research & Services FAQ, reporting the World Meteorological Organization’s definition, describes long-range forecasts as covering 30 days up to one season and forecasting averaged weather parameters: IMD Pune Climate Research & Services FAQ. That is a different task from a daily weather forecast, which aims to describe nearer-term conditions.
A forecast for seasonal rainfall over India therefore cannot tell a farmer, city or traveler exactly when rain will arrive. Seasonal total, regional distribution, onset timing, active or break spells, and daily rainfall are related but distinct forecast targets. IMD lists separate products for these questions, from seasonal outlooks to short- and medium-range forecasts: IMD Seasonal Forecast.
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How forecasters build a seasonal outlook
Seasonal prediction is not a weather map projected months into the future. It draws on historical relationships between climate patterns and monsoon outcomes, as well as numerical climate models that simulate the interacting atmosphere and ocean. IMD’s account of long-range forecasting describes the shift from empirical and regression-based methods toward ensemble forecasting: IMD: Major Innovation in the Science of Meteorology—Long Range Forecasting of Monsoon Rainfall over India.
Statistical methods and coupled climate models
India’s operational strategy uses both a statistical system and a multi-model ensemble of coupled global climate models, according to the Ministry of Earth Sciences: Ministry of Earth Sciences report on monsoon forecast errors. Statistical methods draw on past climate relationships. Coupled models simulate interactions between the ocean and atmosphere that shape large-scale climate conditions.
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Why combine several models?
A multi-model ensemble brings together simulations from several models rather than relying on a single one. Since models represent large-scale climate processes differently, combining them is intended to make the outlook more robust and account for some uncertainty associated with any one model. It does not eliminate uncertainty: models can share biases, miss or simplify important processes, and disagree about how climate drivers will evolve.
Several products answer different questions
IMD’s catalogue includes first- and second-stage seasonal forecasts, a forecast for monsoon onset over Kerala, a mid-season forecast and an August update. It also lists probability forecasts, sea-surface-temperature and ENSO/IOD products, and verification scores: IMD Seasonal Forecast. These products should not be treated as interchangeable. A seasonal total, a probability map and an onset estimate describe different aspects of the monsoon.
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Why long-range predictions remain uncertain
Climate drivers evolve and interact
Monsoon rainfall is influenced by large-scale patterns in the ocean and atmosphere. ENSO in the Pacific and the Indian Ocean Dipole (IOD) are important influences, but neither acts as a simple switch for Indian rainfall. Their effects can change as the season approaches and interact with monsoon circulation. The Ministry of Earth Sciences identifies uncertainty in ENSO’s evolution and its interaction with the IOD as sources of forecast uncertainty: Press Information Bureau: Monsoon Forecast Accuracy and Improvements.
Regional rainfall is harder to resolve
Predicting an all-India seasonal average is not the same as predicting rainfall in a particular state, district or week. A review in the IMD journal MAUSAM describes limits on regional Asian summer monsoon prediction skill arising from difficulties in simulating complex atmosphere–ocean variability and its remote effects on regional climate: MAUSAM: Current status and progress in the seasonal prediction of the Asian summer monsoon. This helps explain why a broad seasonal outlook may be more informative than a local, week-by-week expectation.
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Lead time and forecast target matter
An early seasonal forecast and a later update are made at different points in the year, with different information available. A seasonal rainfall outlook also has a broader target than an onset date or a short-range rain forecast. When comparing forecasts, check the issue stage, geographic and time scale, and whether the product reports a quantitative value or probabilities. IMD’s catalogue distinguishes these product types: IMD Seasonal Forecast.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret reported forecast performance
Forecast-error figures are meaningful only with their publisher, evaluation period, metric and geographic scope. Two Ministry of Earth Sciences figures illustrate why the details matter:
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| Published figure | What it measures | Source |
|---|---|---|
| 5.01% of the Long Period Average (LPA) | Average absolute error of Indian summer monsoon rainfall forecasts over 2015–2024, as reported by the Ministry of Earth Sciences in 2025. | Ministry of Earth Sciences report |
| 1.9% of the LPA | Average absolute error over 2023–2025, as reported by the Ministry of Earth Sciences in a parliamentary answer in 2026. | Press Information Bureau: Monsoon Forecast Accuracy and Improvements |
These figures cover different periods, so they are not directly interchangeable measures of future reliability. Neither should be translated into a universal “accuracy percentage.” The 2026 parliamentary answer also lists actual and forecast rainfall, in percent of LPA, for each year: 95/96 ± 4% in 2023, 108/106 ± 4% in 2024, and 108/106 ± 4% in 2025. It reports that each actual value fell within the stated forecast limits: Press Information Bureau: Monsoon Forecast Accuracy and Improvements.
When assessing any reported score, ask what was forecast, which years were evaluated, what metric was used, and whether the figure applies to India as a whole or a smaller region. A past average error describes performance over its stated period; it does not guarantee the outcome of a future season or a particular location’s rainfall.
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