IMD to forecast monsoon arrival at block level to help farmers plan sowing

IMD's AI-powered block-level monsoon forecasting system was launched on May 12, 2026, targeting over 3,000 sub-districts (blocks) across 16 states with locat...

What Happened

IMD's AI-powered block-level monsoon forecasting system was launched on May 12, 2026, targeting over 3,000 sub-districts (blocks) across 16 states with location-specific monsoon onset information.
The system gives farmers at least 10 days of advance notice of monsoon arrival at the block level, enabling precise sowing decisions for kharif crops such as paddy, soybean, cotton, and maize.
Forecasts are issued every Wednesday in probabilistic format — indicating the likelihood of monsoon progression crossing a given sub-district — rather than as a fixed date, making the uncertainty explicit and actionable.
The system combines AI-based models with extended range prediction systems (weeks 1–4) and statistical post-processing to translate coarse model outputs into block-resolution guidance.
Dissemination is through the Ministry of Agriculture and Farmers' Welfare's framework, which includes Gramin Krishi Mausam Sewa (GKMS) extension channels, SMS networks, and Kisan app interfaces.

Static Topic Bridges

Kharif Cropping Season and Monsoon Dependence

India's kharif season (June–September) is anchored to the southwest monsoon. Over 60% of India's net sown area depends on monsoon rainfall, with paddy, cotton, soybean, groundnut, and coarse cereals among the principal kharif crops.

Southwest monsoon normally arrives at Kerala by June 1 ± 7 days; reaches northwest India by mid-July.
Premature sowing before onset risks crop failure; delayed sowing compresses the growing season and reduces yields.
A 1-week improvement in onset prediction accuracy can raise crop yields by an estimated 3–5% through optimal sowing timing.
India's total food grain production exceeds 330 million tonnes annually; kharif contributes approximately 50% of this.

Connection to this news: The block-level system directly addresses the gap between district-level IMD forecasts and the village-level decisions farmers make about when to sow, reducing wastage from mistimed planting.

Extended Range Prediction Systems (ERPS)

Extended Range Prediction refers to forecasts for the 2–4 week timeframe, bridging short-range (1–7 days) and seasonal outlooks. This "sub-seasonal to seasonal" (S2S) window is the most challenging in meteorology because it lies between weather predictability and climate predictability regimes.

IMD's Multi-Model Ensemble (MME) for extended range uses CGCMs from multiple global centres including ECMWF, NCEP, CFS, and IMD's own MMCFS.
S2S predictability arises primarily from the Madden-Julian Oscillation (MJO) — an eastward-propagating tropical convection system with a 30–90-day cycle that modulates Indian monsoon active and break phases.
AI models improve on pure dynamical ensembles by better capturing MJO teleconnections and local orographic effects.

Connection to this news: The monsoon advance forecasting system is operationally the first S2S product targeted specifically at block-level agricultural decision-making in India, making abstract ensemble outputs actionable for extension workers.

India's Agrometeorology Infrastructure

Agro-Meteorological Advisory Services (AAS) in India are a joint initiative of IMD and the Indian Council of Agricultural Research (ICAR). Over 130 Agro-Meteorological Field Units (AMFUs) collect local crop and weather data to calibrate advisories.

ICAR–CRIDA (Central Research Institute for Dryland Agriculture) coordinates crop-weather modelling.
Gramin Krishi Mausam Sewa (GKMS): block-level agro-weather advisories in local languages, issued twice weekly; covers 3,000+ blocks.
Kisan Call Centres (toll-free 1800-180-1551) disseminate weather-based advisories in 22 languages.
National e-Governance Plan for Agriculture (NeGP-A) digitises these services under the Digital Agriculture Mission.

Connection to this news: The new system integrates with this existing GKMS infrastructure, upgrading the forecast input from coarse district-level data to AI-derived block-level monsoon onset probabilities, substantially raising the advisory quality.

AI in Agriculture (Digital Agriculture Mission)

The Digital Agriculture Mission (DAM), launched in 2021, aims to build a digital public infrastructure for agriculture including AgriStack (farmer registries, land records), and precision farming inputs such as AI-based crop disease detection and yield prediction.

DAM supports AI/ML applications in crop monitoring, soil health, and weather-based advisories.
The National AI for Agriculture programme under MoES and MoA&FW funds development of predictive models.
AI-based weather tools are classified under the "climate-smart agriculture" pillar of India's nationally determined contributions (NDCs) under the Paris Agreement.

Connection to this news: Block-level monsoon forecasting is a flagship output of the AI-in-agriculture agenda, linking the Ministry of Earth Sciences' meteorological capacity with the Ministry of Agriculture's farmer outreach infrastructure.

Key Facts & Data

Coverage: 16 states, 3,000+ sub-districts (blocks).
Advance notice to farmers: Minimum 10 days before monsoon arrival at the block level.
Forecast cadence: Every Wednesday in probabilistic format.
Forecast horizon: Weeks 1–4 (up to 4 weeks ahead).
Implementing agencies: IMD, IITM Pune, NCMRWF.
India's dependence on monsoon: ~60% of net sown area is rain-fed; kharif accounts for ~50% of annual food grain output.
MJO predictability window: ~3–4 weeks, which is the scientific basis for the 4-week forecast horizon.
IMD 2026 monsoon forecast: Below normal — 90–95% of Long Period Average, reinforcing the importance of precise onset timing for farmers.

Note: Articles 90547 and 90695 cover complementary dimensions of the same May 12, 2026 IMD launch event. Article 90547 covers both systems (including the UP high-resolution rainfall pilot); this article focuses on the block-level monsoon advance forecasting system and its agricultural policy context.