Introduction
India's southwest monsoon (June–September) is the lifeline of its agrarian economy, irrigating nearly 52% of the net sown area and influencing ~14% of GDP directly. For the first time in 11 years, the India Meteorological Department (IMD) has forecast a below normal monsoon for 2026, projecting only 92% of the Long Period Average (LPA) of 87 cm. With El Niño conditions developing in the equatorial Pacific and fertiliser supply chains already stressed by geopolitical conflict, the forecast carries significant food security and macroeconomic implications.
"The likely development of El Niño is the primary driver of this year's below-normal monsoon forecast." — M. Mohapatra, Director-General, IMD
Key Concepts
IMD's Rainfall Classification
| Category | Rainfall (% of LPA) |
|---|---|
| Excess | >110% |
| Normal | 96–110% |
| Below Normal | 90–95% |
| Deficient | <90% |
| Drought (unofficial) | <90% |
El Niño — Periodic warming of the Central Equatorial Pacific Ocean. Since 1950, it has emerged 16 times; in 9 of those instances, it depressed India's monsoon rainfall. It typically strengthens in the second half of the monsoon (August–September).
La Niña — The converse of El Niño; associated with above-normal rainfall in India. Currently transitioning to neutral conditions.
Indian Ocean Dipole (IOD) — Oscillation of sea-surface temperatures between the western Indian Ocean (near Africa) and the eastern Indian Ocean (near Indonesia). A positive IOD enhances Indian monsoon rainfall and can partially offset El Niño's suppressive effect.
Background & Context
- 2024 and 2025 recorded surplus monsoon seasons; 2023 was near normal (96% LPA).
- The 2015 below-normal forecast (predicted 93%) underestimated actual deficiency — rainfall fell to 86% LPA, one of India's worst drought years.
- Private forecaster Skymet independently projected 94% of LPA, broadly corroborating IMD's warning.
- IMD's April forecast has historically been reliable for weak monsoon predictions, though it has systematically underestimated drought severity in 2002, 2009, and 2018.
Historical Forecast vs. Actual Rainfall
| Year | IMD April Forecast | Actual Rainfall | Outcome |
|---|---|---|---|
| 2002 | Normal | 81% LPA | Severe drought |
| 2009 | 96–98% (Near Normal) | 77% LPA | Worst drought in a century |
| 2015 | 93% (Below Normal) | 86% LPA | Severe drought |
| 2018 | 97% (Normal) | 91% LPA | Below normal |
| 2023 | 96% (Near Normal) | ~94% LPA | Near normal |
| 2026 | 92% (Below Normal) | — | Forecast |
Factors Moderating El Niño's Impact
Two mitigating factors may partially offset El Niño's negative influence:
- Positive IOD Development — Likely to emerge towards the end of the southwest monsoon season, bringing additional moisture to the Indian subcontinent.
- Below-normal Northern Hemisphere Snow Cover (Jan–Mar 2026) — Reduced snow cover is empirically associated with stronger monsoon circulation over India.
These factors, however, are unlikely to fully neutralise El Niño's suppressive effect, especially during August–September.
Multidimensional Implications
Agriculture & Food Security
- Kharif crops — rice, pulses, oilseeds, cotton — are heavily rainfed and directly vulnerable to rainfall deficiency.
- Disrupted fertiliser supply chains due to the West Asia conflict, compounding input-side stress ahead of the sowing season.
- Risk of elevated food inflation, particularly in cereals and pulses.
Water Resources
- Reservoir levels, groundwater recharge, and hydroelectric generation are directly tied to monsoon quantum.
- Deficient rain intensifies drought stress in already water-scarce regions (Peninsular and Central India).
Economic Impact
- Rural demand contraction affects consumption-driven sectors — FMCG, two-wheelers, rural credit.
- MSP procurement pressure and food subsidy burden on fiscal consolidation.
Governance & Policy
- Drought declaration under the Manual for Drought Management (2016) triggers relief operations, MGNREGS activation, and crop insurance payouts under PMFBY.
- Need for real-time integration of Agro-Meteorological Advisory Services (AAS) and District-level Contingency Plans under ICAR.
IMD's Forecasting Limitations — A Critical Note
IMD's April forecast has a structural optimism bias — in multiple instances (2002, 2009, 2018), actual rainfall was significantly worse than predicted. The May update provides a course correction, but policymakers must build worst-case scenario buffers rather than calibrate response solely to April forecasts.
Conclusion
A below-normal monsoon in 2026, if it materialises at or below the forecast threshold, will test India's agricultural resilience, fiscal management, and disaster preparedness systems simultaneously. While the positive IOD and reduced snow cover offer partial mitigation, the convergence of El Niño, supply chain stress, and post-surplus complacency creates a compounding risk. Strengthening sub-seasonal forecasting, crop diversification in rainfed regions, and pre-positioning of drought relief resources must be the immediate policy priority — underscoring that climate variability management is no longer a reactive function but a core governance responsibility.
Attribution
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GS1GeographyQuick Q&A
What does a ‘below normal’ southwest monsoon mean in the Indian context, and how is it measured?
Significance for India: The southwest monsoon contributes nearly 75% of India’s annual rainfall, making it crucial for agriculture, especially the rainfed Kharif crops like rice, pulses, and oilseeds. A below-normal monsoon can lead to reduced soil moisture, lower reservoir levels, and stress on irrigation systems.
Broader Implications: Beyond agriculture, the monsoon affects GDP growth, rural demand, food inflation, and energy production (especially hydropower). For example, in 2015, a below-normal forecast translated into actual rainfall of 86% of LPA, resulting in one of the worst drought years. Thus, even a slight deviation from normal rainfall has cascading socio-economic consequences in India.
Why does El Niño significantly influence the Indian monsoon, and what are its expected impacts this year?
Historical Correlation: Since 1950, El Niño events have coincided with weakened monsoons in India in 9 out of 16 instances, indicating a strong but not absolute correlation. For example, the severe droughts of 2002 and 2009 were associated with El Niño conditions, where rainfall dropped to 81% and 77% of LPA respectively.
Current Year Implications: The IMD has highlighted the likely development of El Niño during the monsoon season, particularly affecting rainfall in August and September. This timing is critical as it coincides with key crop growth stages. Additionally, external factors such as disruptions in fertilizer supply due to geopolitical tensions (e.g., West Asia conflict) could amplify agricultural stress. Thus, El Niño poses both climatic and economic risks for India.
How can factors like the Indian Ocean Dipole (IOD) and snow cover offset the negative effects of El Niño on the monsoon?
Impact of Snow Cover: Reduced snow cover in the Northern Hemisphere, particularly over Eurasia, can lead to higher land temperatures during summer. This increases the land-sea temperature gradient, which is essential for driving monsoon winds. In 2026, slightly below-normal snow cover is expected to support monsoon circulation.
Combined Effect: While El Niño weakens monsoon systems, the presence of a positive IOD and reduced snow cover can act as mitigating factors. For instance, in some past years, despite El Niño conditions, India received near-normal rainfall due to favorable IOD phases. However, these interactions are complex and not always predictable, highlighting the limitations of monsoon forecasting.
Critically analyze the reliability of IMD’s monsoon forecasts based on past trends.
Limitations and Errors: Despite improvements, there have been notable inaccuracies. In 2002 and 2009, the IMD predicted near-normal monsoons, but India experienced severe droughts with rainfall dropping to 81% and 77% of LPA respectively. Similarly, in 2018, forecasts of 97% rainfall were contradicted by actual rainfall of 91%. These discrepancies arise due to the complex interplay of global climatic factors that are difficult to model precisely.
Critical Evaluation: While IMD forecasts are crucial for policy planning, they should be treated as probabilistic rather than deterministic. Policymakers must adopt a risk-based approach, preparing for worst-case scenarios. Improving data integration, regional forecasting, and real-time updates can enhance reliability, but inherent uncertainties in climate systems will always persist.
Can you provide examples of how below-normal monsoons have impacted India’s economy and agriculture in the past?
Case Study: 2015 Drought: The 2015 monsoon recorded only 86% of LPA rainfall. This affected rural incomes, reduced demand for goods, and slowed GDP growth. Water shortages also impacted urban areas and reduced hydropower generation. The government had to increase spending on relief measures and rural employment schemes like MGNREGA.
Broader Economic Impact: Below-normal monsoons affect multiple sectors:
- Agriculture: Lower yields and farmer distress
- Inflation: Rise in food prices
- Energy: Reduced hydropower generation
- Rural Economy: Decline in consumption demand
As a policymaker, how would you prepare India to mitigate the impact of a below-normal monsoon forecast for 2026?
Medium-Term Strategies: The government should enhance buffer stocks of food grains to control inflation and ensure food security. Schemes like PMFBY (crop insurance) must be effectively implemented to protect farmers’ incomes. Expanding micro-irrigation (drip and sprinkler systems) can reduce dependence on rainfall.
Long-Term Structural Reforms: India must reduce its monsoon dependence by investing in irrigation infrastructure, watershed management, and climate-resilient agriculture. Diversifying rural livelihoods and promoting agro-processing industries can also reduce vulnerability. For example, states like Gujarat have successfully improved water management through check dams and irrigation networks. A holistic approach combining technology, policy, and community participation is essential to build resilience against monsoon variability.
Practice questions
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