IMD Launches AI-Powered Block-Level Monsoon Forecasting to Boost Agricultural Planning

Livemint - Economy · 13 May 2026 · 2 min read
Prelims · Agriculture Mains · GS3 Agriculture High relevance

IMD introduces an AI-driven system to predict monsoon onset at block level across 16 states, addressing India's rain-dependent agriculture challenges amid a forecasted below-normal monsoon (92% of LPA).

Key points

IMD is deploying an AI-powered system to forecast monsoon arrival at the block level, covering 3,000+ sub-districts across 16 states, including Gujarat, Maharashtra, and Uttar Pradesh.

The system provides 10-day advance forecasts using AI models, extended-range prediction tools, and statistical techniques, disseminated via mobile apps, SMS, and agricultural extension networks.

This initiative targets rain-fed agriculture regions, where 45% of India's net sown area lacks irrigation, making monsoon timing critical for crop productivity.

[GS3-Economy] The forecast comes as IMD predicts a below-normal monsoon (92% of LPA), heightening risks for India's farm sector, which contributes ~15% to GDP and employs nearly half the workforce.

The Uttar Pradesh pilot offers 1-km resolution rainfall forecasts using AI downscaling, with plans for national expansion as observational infrastructure improves.

This connects to GS2-Governance by enhancing digital agriculture initiatives and agrometeorological advisory services, aligning with the National Mission on Sustainable Agriculture.

Farmers can optimize sowing windows, crop selection, and input application, reducing uncertainty that often leads to delayed sowing or premature planting losses.

[GS3-Environment] The system addresses increasing weather variability and extreme climate events, supporting climate-resilient agriculture under India's National Action Plan on Climate Change.

Way Forward: Expand AI-weather forecasting infrastructure to all states, integrate with PM-KISAN for targeted advisories, and establish farmer training programs on interpreting probabilistic forecasts.

Key terms

Long Period Average (LPA)

The 50-year average rainfall (87 cm) used as a benchmark for monsoon performance in India. For UPSC, deviations from LPA impact agricultural GDP, inflation, and rural demand—critical for economy-related questions.

Rain-fed Agriculture

Farming systems dependent on natural rainfall rather than irrigation, covering ~45% of India's net sown area. UPSC relevance lies in its vulnerability to climate change and linkage with food security, farmer distress, and rural economy.

Extended Range Prediction System (ERPS)

IMD's dynamical model providing weather forecasts for 10-30 days. Strategically important for UPSC as it bridges short-term and seasonal forecasts, enabling agricultural planning and disaster preparedness.

Agrometeorological Advisory Services

Government initiatives providing weather-based crop advisories to farmers. UPSC focus areas include their role in climate adaptation, integration with Krishi Vigyan Kendras, and contribution to doubling farmers' income.