This monsoon season has brought severe destruction to India’s northern states. There were cloudbursts in Uttarakhand, flash floods in Himachal Pradesh, landslides in Jammu and Kashmir, and Punjab saw its worst flood since 1988.

In Punjab, some locations recorded more than 1,000 per cent of their 24-hour normal rainfall, according to IMD data. In fact, between August 28 and September 3, northwest India received rainfall around 180 percent above average. 

State loss estimates are already staggering. Uttarakhand projects damages of around Rs 3,000 to 5,000 crore. Himachal Pradesh has reported losses above Rs 4,000 crore, alongside more than 366 deaths. In the Jammu division of J&K, torrential rains in mid-August left 138 people dead in just 12 days, while floods damaged a section of the Fourth Tawi Bridge and pushed infrastructure losses beyond Rs 100 crore. Agriculture was also badly hit in Jammu, with crops across about 1.40 lakh hectares damaged, including more than 90,000 hectares severely affected. In Punjab, the government pegged the losses at around Rs 14,000 crore.

These are also warning signals of a deeper financial crisis – one that touches households, insurers, banks, and governments alike. Most losses are uninsured. Families rebuild with savings or new loans; banks are saddled with non-performing assets from infrastructure projects and agricultural loans; and state governments scramble for ad hoc relief. 

Unless India develops better ways to finance resilience, the country risks entering an age of “uninsurability” in its climate-vulnerable regions – where disasters strike so often and so severely that traditional insurance and credit systems no longer function.

Why the rains were so extreme this year

The climate science behind 2025’s devastation matters as much as the numbers. A warmer atmosphere carries more moisture from the Indian Ocean and Arabian Sea, and when storms organise, this translates into exceptionally heavy rainfall – more water in less time. The monsoon has shifted from steady, season-long rains to long dry spells punctuated by intense bursts.

Thermodynamic analysis shows that, in a warming world, extreme precipitation scales positively with temperature once cloud-cooling is removed, explaining more heavy rain over India.

This year, north India experienced an unusual overlap of weather systems: monsoon lows from the Bay of Bengal were pushed further north by moisture-laden winds from the Arabian Sea, where they converged with a greater number of Western Disturbances (WDs). WDs – normally confined to winter – lingered into summer and arrived more often than usual. Research links this to the delayed retreat of the subtropical jet, which steers WDs unusually south into India and makes them persist later into the monsoon. Some scientists connect this delay to rapid Arctic warming, which weakens the pole-to-tropics temperature contrast, making the jet wavier and slower to shift – raising the chance of WD–monsoon overlap.

These interactions enhanced lift and instability, further amplified by a northward-shifted low-level jet that carried Arabian Sea moisture into north India. Over the Himalaya, this moist air was forced up steep slopes, triggering deep convection that built towering storm clouds. The result was cloudbursts – intense, localised downpours that can overwhelm rivers and valleys within hours. At the same time, mountain processes are also shifting: rain-on-snow events trigger rapid melt, thawing permafrost weakens slopes, and glacial-lake or landslide-dam bursts unleash floods even without local storms.

In short, natural variability and global warming combined this year to amplify rainfall extremes and flood risks across north India.

How science can reduce losses

Hazards are only part of the story. Losses explode when exposure and vulnerability are high. Reckless development has reduced natural rainwater outlets, with hotels on floodplains, roads cutting steep slopes, and deforestation weakening hillsides. This does not mean development must stop, but it must be smarter. The challenge is to build intelligently, guided by decision-useful science that can feed directly into regulatory approval, as well as the design and planning of climate-resilient infrastructure.

Decision-useful science goes beyond technical reports. It means science – or information on risk – must be reliable and usable at the policy, regulatory, financial (banks and insurers), and community level. Impact or catastrophe models for floods, for instance, need to be transparent, co-designed with end-user input, and capable of translating risk numbers into risk narratives – so that decision-makers can grasp not only the probabilities or uncertainties behind loss but also what those losses mean on the ground. In practice, this could mean co-designing asset-level, multi-scenario risk narratives using location-specific hazard–exposure–vulnerability mapping. For example, identifying flood-prone hotspots and possible mitigation options at the scale of individual valleys before approving new infrastructure projects.

As the future brings uncertainty, ensemble forecasts that capture multiple possible hazard scenarios are essential. Strengthening our physical-science understanding of climate through high-resolution downscaling combined with AI–ML can simulate deep convective processes more realistically and help detect shifts in local weather patterns. Linking these tools with impact-assessment frameworks will enable hyperlocal early warning systems. That is an urgent need of the hour.

The financial strain

The ripple effects of climate disasters extend across the financial system. Households face repeated rebuilding costs that erode savings, while crop and livestock losses force additional borrowing. Insurers see payouts rise sharply, and reinsurance becomes costlier, pushing premiums higher and edging risky areas closer to being uninsurable.

Banks struggle as collateral loses value: project loans for hydropower schemes or tourism facilities can become stranded, and non-performing assets rise. Governments then step in as insurers of last resort, shouldering the expense of compensating farmers for crop losses or rebuilding infrastructure. Uttarakhand alone has asked the Centre for Rs 5,702 crore in relief. Globally, there are precedents: insurers have already withdrawn from wildfire-hit California and cyclone-prone Queensland, forcing governments to intervene. India risks a similar outcome in its vulnerable regions if losses remain unmanaged.

Financial instruments to share the risk

everal tested instruments could help India prepare financially for future disasters. One starting point is public-asset insurance, since most government infrastructure – such as roads, bridges, and hospitals – remains uninsured. Other countries, including Turkey and Indonesia, have insured public infrastructure through national programmes, ensuring that reconstruction funds flow quickly.

Another promising tool is parametric insurance, which pays out when a measurable threshold – such as rainfall intensity, river level, or wind speed – is crossed. Nagaland pioneered such a state-wide rainfall cover in 2024, which triggered a Rs 1 crore payout in 2025. Similar products are being tested for extreme heat, with index-based insurance already reportedly paying thousands of informal workers when temperatures exceed agreed limits. For mountain agriculture and small towns, parametric flood or landslide covers could provide the same rapid liquidity.

Larger systems can be built through risk pools. Australia’s Cyclone Reinsurance Pool, backed by the federal government, has kept coverage available in high-risk regions while reducing premiums. A flood or landslide pool for Himalayan states could provide similar stability. At the sovereign level, catastrophe bonds – which transfer risk to international investors – are already being explored in India with World Bank support. Contingent credit lines are another option: the World Bank’s Catastrophe Deferred Drawdown Option (Cat DDO) disburses quickly after a disaster declaration; the Dominican Republic used it to unlock $150 million following severe floods.

Crop insurance schemes like the Pradhan Mantri Fasal Bima Yojana and the Restructured Weather Based Crop Insurance Scheme (RWBCIS) remain important but have faced settlement delays and basis-risk issues. This underscores the need for more crop- and location-specific granularity. Expanding index-based covers – as in IWMI’s Bihar flood pilots and SEWA’s heat insurance for women across Rajasthan, Gujarat, and Maharashtra – can deliver faster, more targeted protection for farmers and rural incomes.

Science and finance must work together

Financial risk-management instruments only work if grounded in robust, transparent climate-risk data. This means linking climate models with stronger observation networks – ground stations in mountains, glacier monitoring, drones, and satellites – feeding into forecasts and AI systems that deliver location-specific insights. 

Open-access, user-friendly datasets on the exposure of critical infrastructure and assets can then support risk modelling and planning. By improving cloudburst and landslide prediction, such integrated data can make insurance triggers credible, guide financial stress testing of assets and portfolios, and translate abstract climate risk into practical, actionable narratives – whether for adjusting premiums, strengthening infrastructure, or issuing targeted evacuation alerts.

Development is vital for India’s progress, but it has to come with a recognition that climate hazards are intensifying, that exposure and vulnerability are often shaped by human choices, and that financial resilience is as essential as physical resilience. 

Practical steps are within reach: insuring key public assets, expanding parametric cover, building regional risk pools, exploring catastrophe bonds, arranging contingent credit, and investing in transparent data, research and development, and skills in climate-risk and financial analytics through stronger industry–academia collaboration.

These measures can help build a more climate-resilient India, where the next disaster need not trigger a financial crisis. With the right systems in place, insurers can keep insuring, banks can keep lending, and households and states can rebuild more quickly.

The writer is a research fellow in climate finance analytics at the University of Leeds, UK.

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