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?

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.