Critically examine the evolution of monsoon forecasting in India from sub-divisional to block-level resolution. How does AI integration improve forecast accuracy, and what are its limitations in El Niño years?

Q. Critically examine the evolution of monsoon forecasting in India from sub-divisional to block-level resolution. How does AI integration improve forecast accuracy, and what are its limitations in El Niño years? (15 marks, 250-350 words)

Indian monsoon forecasting has travelled from country-wide seasonal averages to sub-district precision, culminating in IMD's AI-enabled block-level monsoon onset model (May 2026). The shift is a genuine advance, but its coverage and its skill in anomalous years remain unproven.

The trajectory of resolution - IMD has issued an operational long-range forecast in two stages (April, end-May) since 2003, yielding a single all-India seasonal figure against a Long Period Average of 87 cm (1971–2020) [2][4] — valuable for policy, but not for a sowing decision. - The Multi-Model Ensemble approach built on the Monsoon Mission Climate Forecast System (MMCFS) improved seasonal skill [2], and district-level medium-range forecasts followed. - The new onset model closes the last gap: probabilistic block-level onset predictions up to four weeks ahead, piloted across parts of 13 States and disseminated to over 3.8 crore farmers via mKisan in five languages [1].

A critical assessment - Coverage is phased, not pan-India, raising questions of equitable access across States. - Onset ≠ quantum: the model predicts when rain arrives, not how much falls — crop insurance (PMFBY) and drought planning need both. - Benefits depend on last-mile delivery through KVKs and agro-met advisories being trusted and acted upon.

How AI improves accuracy - It blends numerical models (ECMWF's AIFS, NeuralGCM) with roughly 125 years of IMD rainfall records, learning local onset signatures that coarse physics-based grids smooth away [1]. - Outputs are probabilistic and weekly, with an error margin of about four days — communicating likelihood rather than a false-precision date [1].

Limitations in El Niño years - MMCFS indicated El Niño development for 2026, with seasonal rainfall revised down to 90% of LPA [3]; El Niño typically suppresses monsoon rainfall. - AI learns from history, yet El Niño events are few and non-identical — training data is thinnest precisely when forecasts matter most. - A correct onset call can still mislead if followed by prolonged dry spells.

Block-level forecasting genuinely democratises climate information for the farm household. Consolidating it requires nationwide coverage, onset forecasts paired with rainfall-quantum and dry-spell outlooks, and honest communication of uncertainty — so that a below-normal year strengthens rather than erodes farmer trust, advancing climate-resilient agriculture and SDG-2 and SDG-13.

(~330 words)

Sources: 1. Artificial Intelligence (AI) Transforming Indian Agriculture — PIB, Government of India — AI-blended onset model (NeuralGCM + ECMWF AIFS + ~125 years IMD data), 13-State pilot, mKisan dissemination to 3.88 crore farmers, four-week probabilistic lead time 2. Long Range Forecast for the 2026 Southwest Monsoon Season Rainfall — PIB, Ministry of Earth Sciences — two-stage LRF since 2003; MME/MMCFS framework 3. Updated Long Range Forecast for Southwest Monsoon Seasonal Rainfall, June–September 2026 — PIB — El Niño development signalled by MMCFS; rainfall revised to 90% of LPA 4. Ministry of Earth Sciences, Long Range Forecast for the 2026 Southwest Monsoon (PDF) — LPA of 87 cm for 1971–2020