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What the IIT Madras platform is trying to forecast
The work is described as a team effort at the Centre of Excellence for Studying Critical Transitions in Complex Systems at IIT Madras, led by Professor R. I. Sujith. India Today reported the platform’s longest lead time as up to 10 months for an outlook on the Northeast Monsoon, which is particularly important to Tamil Nadu.
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It is not one model doing every job. The report describes separate methods for short-range weather, monsoon onset and withdrawal, and seasonal rainfall totals:
| Platform component | Question it addresses | Approach described |
|---|---|---|
| Short-range weather | What weather is expected over the near term? | Dynamical models, as described by India Today; the report does not state a specific lead time. |
| Onset and withdrawal | When does monsoon activity make a large-scale transition? | Complex-network analysis of climate-station data and phase transitions. |
| Seasonal rainfall | How much rain may fall over a season? | Separate AI and machine-learning systems, including the team’s reported long-range Northeast Monsoon outlook. |
These are different forecast targets. A seasonal estimate concerns accumulated rainfall across a period; it does not tell a farmer when a specific storm will arrive or how much rain will fall on a particular day. The team’s explanation, as reported by India Today, is that slower-changing influences such as ocean temperatures may help provide information at longer horizons. That rationale does not, on its own, establish the accuracy of a 10-month forecast.
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What a monsoon “transition” means
The onset-and-withdrawal work looks for abrupt changes in broad patterns of rainfall rather than treating the monsoon as a single switch that flips everywhere at once. The team’s paper describes rainfall-onset clusters coalescing through two large-scale transitions: first over Northeast India, then over the Indian peninsula. It also reports that the peninsula transition depends on characteristics of the earlier Northeast onset.
The paper, Climatic Phase Transitions Unravel the Onset and Withdrawal of Indian Monsoon, was submitted to arXiv on 19 March 2025 and revised on 20 March 2025. It is a preprint. Its described analysis concerns onset and withdrawal; it is not an independent test of the platform’s 10-month seasonal rainfall outlook.
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What the reported 2026 forecast comparison shows
India Today reported two Southwest Monsoon comparisons for 2026. These are useful as a snapshot of forecasts and outcomes, but they are not a multi-year verification record.
| 2026 Southwest Monsoon measure | Reported figure | Qualification |
|---|---|---|
| Seasonal rainfall total | 759.4 mm, 13% below normal | India Today attributed the final total to the India Meteorological Department (IMD). |
| Team’s rainfall estimate | 761 mm, about 12.5% below normal | India Today said the estimate was made about five months ahead. |
| Delayed-onset prediction | 12 days ahead | India Today reported that the team forecast a delayed onset 12 days in advance. |
The reported total and estimate are close, but one close outcome cannot show whether a forecasting system is reliably better than chance or other forecasts. The report also said the team’s 10-month Northeast Monsoon outlook called for normal rainfall over Tamil Nadu and was in line with IMD’s peninsular outlook issued on 30 September 2026. That agreement is a comparison of outlooks, not a completed test of the Northeast Monsoon’s eventual rainfall.
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What IMD says about a 10-month monsoon outlook
India Today quoted IMD Director General of Meteorology Mrutyunjay Mohapatra saying that current monsoon-rainfall forecast skill does not extend to a 10-month horizon. He said a claim at that range would need validation and evidence that it works. The report describes IMD’s Southwest Monsoon forecast window as shorter and says it combines global dynamical models; it does not provide a like-for-like, multi-year skill score comparing IMD with the IIT Madras seasonal product.
That distinction matters: forecasts can differ in the season and region covered, the quantity being predicted, lead time, method, uncertainty information and operational purpose. Without comparable verification across years—including years with unusual climate conditions—there is no sound basis in the reported information for declaring either outlook the more accurate one.
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What evidence would establish forecasting skill?
A useful test must evaluate forecasts issued before the outcome was known, using clearly defined targets and a consistent scoring method. A reader assessing a long-range product would want to see:
- A dated archive of forecasts, including updates, so each forecast can be matched to what was ultimately observed.
- Precise definitions of the region, season, rainfall measure and meaning of terms such as “normal.”
- Probabilities or uncertainty ranges where available, rather than only a single rainfall number.
- Results across multiple years, including unusual climate conditions, compared with an appropriate baseline and other forecasts for the same target.
Neither a multi-year evaluation nor a calibrated skill score for the 10-month product is presented in the reporting described here. Sujith, as quoted by India Today, has said that sustained skill—including in unusual conditions—would provide strong scientific evidence of the platform’s value. Skymet meteorologist Mahesh Palawat was quoted as saying at least five or six years, and perhaps a decade, would be needed to distinguish a persistent signal from a fluke. Those are views on the evidence needed, not measurements of this platform’s performance.
How early information could help—and what it cannot promise
For Tamil Nadu, an earlier seasonal rainfall outlook could inform decisions such as crop choice and reservoir planning. India Today presented drought-resilient crop selection and water management as potential uses, not demonstrated benefits of this platform. Those decisions still depend on local conditions and later updates; a seasonal outlook is not a guarantee of rainfall at a particular place.
Forecasts carry errors. Sujith told India Today, “No weather or climate forecasting model is 100 per cent accurate,” and described forecast errors as inherent to scientific prediction. For a long-range outlook to be useful in practice, decision-makers need to understand its uncertainty and how well it has performed—not just whether one season’s estimate came close.
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