Evaluate the institutional and scientific challenges facing India's seasonal monsoon forecasting system. Suggest measures to improve sub-seasonal to seasonal (S2S) prediction.

Seasonal forecasting by the India Meteorological Department (IMD), under the Ministry of Earth Sciences, underpins sowing, water and disaster planning. The 2026 season — where El Niño's onset was tracked months ahead but the monsoon's erratic swings were not — exposes real limits alongside genuine institutional gains.

Strengths worth acknowledging

• Under the Monsoon Mission, models for short-to-medium range (1–10 days), extended range (10–30 days) and seasonal scales are now in operational use [1].
• Mission Mausam and the Arka (IITM) and Arunika (NCMRWF) supercomputers have expanded high-performance computing capacity [1]; NCAER estimated large economic returns on this investment [2].

Scientific challenges

• Large-scale drivers such as ENSO are comparatively predictable, but meso-scale convection, cloudbursts and intra-seasonal dry spells are not — a deficit season can still bring destructive urban flooding.
• Competing drivers: IMD tracked El Niño alongside an Indian Ocean Dipole turning positive only late in the season, complicating the signal [3].
• Coarse model resolution, sparse ocean–atmosphere observations and limited land-surface data weaken data assimilation.

Institutional challenges

• Repeated mid-season revisions of the 2026 outlook, from the April Long Range Forecast to the late-July update, reflect operational strain [3][4].
• Forecasts framed as national Long Period Average percentages are of limited use to a district-level farmer.
• Weak last-mile convergence between IMD, agriculture departments, urban drainage bodies and disaster authorities.

Way forward

• Scale up dedicated S2S modelling and higher-resolution ensembles under Mission Mausam [1].
• Densify the observation network — Doppler radars, ocean buoys, automatic weather stations — to improve assimilation.
• Shift to probabilistic, impact-based, block-level advisories rather than averages.
• Institutionalise forecast-to-action links with NDMA's drought framework and crop contingency plans [5].

On balance, India's forecasting system is scientifically credible on seasonal drivers but institutionally and computationally stretched at the sub-seasonal scale. Investing in observation density, S2S science and last-mile delivery would convert forecasts into resilience, advancing climate-adaptation goals under SDG-13.