Below-normal rainfall but exceptional flooding with localised extremes and economic impacts
On 13 April 2026, the India Meteorological Department (IMD) forecast below-normal southwest monsoon rainfall, estimating a seasonal total of 90–95% of the Long Period Average (LPA) (IMD, 2026a). Rainfall between June and September 2026 totalled 759.4 mm, equivalent to 87% of the LPA and below the forecast range. June was particularly dry, followed by a partial recovery in July before deficits re-emerged in August and September (IMD, 2026b).
Despite the national rainfall deficit, the season featured several exceptional rainfall events. A total of 388 extremely heavy rainfall events, defined as more than 200 mm of rainfall within a 24-hour period, were recorded across Bihar, Orissa, Kerala, Telangana, West Bengal, Himachal Pradesh, Gujarat, Uttaranchal, Madhya Pradesh, Konkan and Goa (IMD, 2026b). Among the most notable was the record-breaking 24-hour total of 1,060 mm recorded in Umergam, Valsad district. These events illustrate the intensity and geographical spread of extreme rainfall during the season.
These fluctuations show that monsoon impacts depend not only on onset and withdrawal dates but also on how rainfall is distributed throughout the season. This distribution influences agriculture, reservoir storage, flood risk and drought-like conditions.
The 2026 monsoon demonstrates why seasonal rainfall totals alone cannot fully capture flood risk: even in a below-normal rainfall year, localised extremes caused significant impacts across multiple regions of India. As climate change intensifies the hydrological cycle and amplifies the consequences of natural climate variability, historical averages alone are insufficient for assessing risk. High-resolution flood modelling and asset-level risk intelligence are therefore increasingly important for building resilience, protecting infrastructure and informing investment decisions.
JBA Risk Management provides detailed flood risk intelligence through our industry-leading flood hazard maps and modelling systems. Our rigorous, science-based approach delivers high-resolution data that helps organisations identify localised hotspots, stress-test assets and optimise long-term portfolio risk management.
Timeline of flood events and impacts
Successive episodes of extreme rainfall drove severe flooding across several Indian states from June to September 2026. The most significant impacts were reported in Assam, Maharashtra, Gujarat, Kerala, Odisha, Uttar Pradesh, Uttarakhand, and Andhra Pradesh, resulting in widespread inundation, infrastructure damage, agricultural losses, and disruption to industrial operations. Over the southwest monsoon period, the highest cumulative rainfall reported nationally was 3,182.5 mm in Dadra and Nagar Haveli, followed by Raigad (2,940 mm) and Valsad (2,875.1 mm) (The Hindu, 2026a).
The table below offers a summary of flood events and impacts.
Notable historical events
The 2026 floods are the latest in a series of damaging monsoon events in India. Flooding accounts for around 41% of the country’s natural catastrophes and has substantial social and economic consequences (SBI Research Ecowrap, 2023). Table 2 summarises notable flood events between 2020 and 2025 and their reported economic losses.
In 2025, intense monsoon rainfall caused widespread flooding across Punjab, affecting around 200,000 people, damaging more than 500,000 acres of crops and washing away approximately 2,500 bridges. The event resulted in an estimated USD 1.45 billion in losses (Government of Punjab, 2025).
More widespread flooding occurred in 2020, when prolonged monsoon rainfall affected large parts of the country. More than one million people were affected and around 29,000 people were evacuated to relief shelters across northeastern India (The Guardian, 2020). Total economic losses from the event were estimated at USD 5.52 billion (SBI Research Ecowrap, 2023).
Past flood events, together with the monsoon 2026 floods, demonstrate India’s continued susceptibility and vulnerability to the South Asian monsoon season. As exposure continues to grow and rainfall variability increases, understanding where flood risk is concentrated will become increasingly important for insurers, reinsurers, financial institutions and government agencies.
El Niño and variability in India’s monsoon rainfall
A key climate influence during the 2026 monsoon was the development of El Niño conditions in the tropical Pacific Ocean. El Niño occurs when sea surface temperatures in the central and eastern Pacific become warmer than average, altering atmospheric circulation and influencing weather patterns globally (WMO, 2026).
In India, El Niño has historically been associated with weaker monsoon circulation and below-average seasonal rainfall and was identified by the IMD as a major factor in its forecast of deficient rainfall for 2026 (IMD, 2026b). However, El Niño is only part of the picture. Other climate and atmospheric processes, including the Indian Ocean Dipole (IOD), Madden-Julian Oscillation (MJO), monsoon low-pressure systems and regional topography, can influence when and where rain falls (WMO, 2026). These interacting factors help explain how a monsoon with below-average rainfall nationally can still produce extreme rainfall and flooding locally.
Why did intense rainfall occur despite a deficient monsoon?
A deficient monsoon does not necessarily mean lower flood risk. Seasonal rainfall totals are measured across four months, while flooding can be triggered by exceptionally heavy rain falling within just a few hours or days.
Several atmospheric processes can generate these extreme rainfall events, including strong moisture transport from the Arabian Sea and Bay of Bengal, the timing and location of monsoon systems, and orographic uplift over mountainous terrain (Jose et al., 2026). Research also indicates that El Niño years can experience fewer rainy days overall while still seeing an increased likelihood of extreme daily rainfall in some parts of India (Hill et al., 2024). Longer dry periods can therefore be interrupted by short, intense rainfall events capable of causing flash flooding, urban inundation and landslides.
Climate change
While El Niño has historically been associated with below-average monsoon rainfall, recent studies suggest that rising global temperatures may also influence rainfall variability and the occurrence of extreme precipitation events.
Climate change affects the background environment in which weather systems develop. A warmer atmosphere can retain more water vapour, increasing the potential intensity of rainfall when favourable meteorological conditions occur. Studies have reported increasing variability in Indian monsoon rainfall, characterised by shifts in the timing, distribution and intensity of rainfall, including an increase in the frequency of extreme rainfall events in some regions (Roxy et al., 2017; IPCC, 2021).
Zhao et al. (2026) found that greenhouse warming may strengthen the relationship between El Niño and Indian monsoon droughts, suggesting that future El Niño events could have a greater influence on monsoon rainfall than in the past.
Nevertheless, caution is required when attributing observed monsoon behaviour to any single driver. El Niño, regional ocean-atmosphere interactions, monsoon dynamics and longer-term climatic trends may all influence rainfall patterns and their variability.
As such, the 2026 monsoon offers a useful case study for exploring how large-scale climate variability and evolving climatic conditions can affect the timing, distribution and intensity of rainfall across India.
Industry insight
The monsoon 2026 floods highlight the challenge of managing flood risk during a variable monsoon season. For insurers, reinsurers and financial institutions, the flood events demonstrate that significant losses can occur even when seasonal rainfall is forecast to be below average.
Agricultural impacts show how the season’s risks extended beyond flood alone. Earlier in the season, prolonged heatwaves and a delayed monsoon affected wheat, cotton and soybean production (The Guardian, 2026; Global Agriculture, 2026). During the July floods, more than 3,800 hectares of agricultural land were inundated and over 37,000 livestock were affected (The Watchers, 2026). Together, these impacts can disrupt agricultural production, supply chains and local economies, reinforcing the need for crop insurance and resilience measures discussed below.
The Indian Government has introduced measures to improve climate resilience, including maintaining rice reserves, encouraging less water-intensive crops and promoting enrolment in the Pradhan Mantri Fasal Bima Yojana (PMFBY) crop insurance scheme (BBC, 2026; Global Agriculture, 2026).
The floods occurred at an important time for India’s insurance market, which comprises 32 general insurers and 13 reinsurers (General Insurance Council, 2026). The Insurance Regulatory and Development Authority of India (IRDAI) recently granted licences to two additional insurance and reinsurance companies (The Economic Times BFSI, 2026). Offshore capacity has also expanded through the International Financial Services Centre (IFSC) at GIFT City, which hosts more than 36 IFSC Insurance Offices underwriting direct USD-denominated treaty business (Business Standard, 2026).
According to IRDAI, India’s general insurance market saw a 28% decline in premium volumes between Q1 FY26 and Q1 FY27, largely due to aggressive discounting in the fire and STFI segments (The Economic Times, 2026). The regulator has warned insurers against offering discounts of up to 99% on large industrial risks, citing concerns over weakened underwriting discipline and reduced resilience to future catastrophe losses.
IRDAI has also introduced measures under the Policyholders’ Education and Protection Fund (PEPF) framework to improve policyholder awareness and promote technology-enabled early warning systems for extreme weather events (IRDAI, 2026). These initiatives highlight the importance of both financial protection and pre-event preparedness.
As India's insurance market continues to expand towards the government's target of achieving "Insurance for All" by 2047 (PIB, 2026), this year’s monsoon flood events reinforce the importance of robust flood risk information. JBA's high-resolution flood data, catastrophe models and early warning capabilities support flood risk assessment, flood forecasting, and proactive risk mitigation efforts. This advanced flood risk insight helps underwriting, portfolio management and long-term resilience decisions.
Conclusion
The 2026 southwest monsoon recorded an overall rainfall deficit, but the seasonal average masked highly localised extreme rainfall across states including Maharashtra, Gujarat, and Uttar Pradesh. The season illustrates the interaction between natural climate variability, including El Niño, and longer-term climatic changes. As discussed above, attributing individual rainfall events to a particular driver requires caution.
The season demonstrated how quickly the South Asian monsoon can shift from prolonged dry conditions to widespread flooding. The impacts included loss of life, damage to homes and disruption to transport, manufacturing and agriculture. It further highlighted the vulnerability of communities and businesses to concentrated short-duration rainfall.
For insurers, reinsurers and financial institutions, the 2026 southwest monsoon floods reinforce the need to assess flood risk beyond national seasonal forecasts. As climate variability and exposure evolve, high-resolution data and models can support more informed underwriting, pricing and capital management decisions.
JBA Risk Management's flood models, high-resolution flood maps and catastrophe modelling capabilities support underwriting, portfolio management and decision-making by helping insurers, reinsurers and financial institutions understand evolving flood risks across India. JBA's India Crop Model complements these capabilities by supporting the assessment of agricultural losses under schemes such as PMFBY. Together, these tools provide a scientifically robust basis for assessing risk in an increasingly uncertain climate.
