India is harnessing artificial intelligence to deliver climate intelligence at the grassroots level, transforming how millions of farmers and vulnerable communities prepare for extreme weather. From cyclone forecasts that now predict storm paths up to 96 hours before landfall to AI-enabled landslide warnings that detect ground movements with over 90 percent accuracy, the country is embedding machine learning into its climate adaptation strategy. The shift represents a fundamental change in how climate science reaches people who need it most .

How Is India Using AI to Predict Extreme Weather Events?

India's meteorological infrastructure has undergone a dramatic upgrade in recent years. The India Meteorological Department (IMD) now operates high-performance computing systems with 22 PetaFLOPS of capacity, a measure of raw computational power roughly equivalent to performing 22 quadrillion calculations per second . Around 10 percent of this computing strength is dedicated specifically to AI workloads, accelerating the development of next-generation forecasting models.

The practical results are striking. Indian researchers have deployed transformer-based neural networks, a type of AI architecture originally developed for language processing, to forecast monsoon behavior up to 18 days in advance . Comparative studies of global AI models have shown significant improvements in predicting cyclone paths, with some systems now achieving accuracy up to 96 hours before landfall, compared to earlier capabilities . This extended warning window gives communities crucial time to evacuate and protect critical infrastructure.

The IMD has also adopted the Advanced Dvorak Technique, an AI-assisted tool that estimates cyclone intensity and improves storm tracking in real time. At the institutional level, the Indian Institute of Tropical Meteorology in Pune has established a virtual center for AI-based weather tools, while the IMD has partnered with India's top engineering institutes, space agency, and defense research organizations to deepen research and training in AI and machine learning .

What Makes India's Village-Level Forecasting System Different?

Perhaps the most transformative development is the expansion of hyperlocal weather forecasting to nearly every village council across India. Farmers can now access localized forecasts covering temperature, rainfall, humidity, and wind conditions through mobile applications, enabling informed decisions on sowing, irrigation, and harvesting .

In May 2025, the government launched the Bharat Forecasting System (BharatFS), an indigenous AI-powered model delivering forecasts at a 6-kilometer resolution, an improvement over the earlier 12-kilometer grid . The system can predict rainfall up to 10 days in advance, significantly improving agricultural planning and disaster readiness for rural communities. Additional AI-based tools under development include MausamGPT, a chatbot designed to provide climate and weather advisories, as well as deep learning systems to forecast fires, lightning, fog, and thunderstorms .

How Are AI Systems Protecting India's Most Vulnerable Communities?

Beyond weather forecasting, AI is being deployed across multiple climate risks that threaten India's most vulnerable populations. Consider the following applications currently operational or in advanced development:

• Landslide Early Warning: An indigenous AI-based system installed at more than 60 locations in Himachal Pradesh uses low-cost sensors and machine learning models to detect millimeter-level ground movements with over 90 percent accuracy, providing alerts up to three hours before slope failure .
• Flood Forecasting: The Indian Land Data Assimilation System (ILDAS), supported by the Indian Space Research Organisation (ISRO), integrates AI models to improve river basin management in the Ganga and Brahmaputra regions, enhancing preparedness in flood-prone areas .
• Coastal Monitoring: AI-powered systems are being deployed to assess sea-level rise and identify high-risk zones along India's vast coastline, aiding urban planning and long-term adaptation strategies .
• Forest Surveillance: AI-enabled surveillance systems detect forest fires, illegal encroachments, and unauthorized tree felling, while also preventing human-wildlife conflicts by identifying animals straying beyond forest boundaries .
• Air and Water Quality: Researchers at Indian Institute of Technology Kharagpur have developed an AI-based model to detect arsenic contamination in groundwater along the Ganga basin, supporting safer drinking water planning under national initiatives .

These systems collectively extend early warning lead times, reduce infrastructure losses, and protect vulnerable communities across India's climate-vulnerable regions .

What Is the Broader Context for AI in Climate Science?

India's push to integrate AI into climate science reflects a global shift in how weather and climate intelligence is being generated and deployed. At the Met Office in the United Kingdom, experts gathered recently to discuss how advances in supercomputing, cloud infrastructure, and artificial intelligence are transforming weather and climate intelligence . The discussion highlighted a critical challenge: the pace of performance improvement in traditional supercomputers has slowed, forcing organizations to rely on complementary technologies like cloud services and AI to sustain innovation .

"Supercomputers have been a real engine in driving innovation and allowing us to do more and more science within our organisation. That speed up and performance improvement characteristic has changed dramatically over the past two years and so we're having to rely upon other ways to make sure we keep that pace of scientific innovation going, and that's from leaning into our cloud partners and using all the other services they have in and around the supercomputer in the cloud," said Richard Lawrence, Principal Fellow for Technology at the Met Office.

Richard Lawrence, Principal Fellow for Technology, Met Office

AI's real value in weather and climate services often lies in enabling entirely new applications that blend traditional physics-based models with emerging machine learning techniques. Graphics Processing Units (GPUs), which now dominate the world's latest supercomputers, have enabled exascale computing optimized for AI workloads . However, experts emphasize that AI will not replace traditional physics-based approaches; instead, both methods are needed working together .

How Can Organizations Balance AI's Energy Use With Climate Benefits?

The environmental impact of AI training and deployment remains a critical consideration. Training large AI models can be very energy intensive, raising legitimate questions about sustainability. However, experts argue that the context matters significantly .

"AI may be energy intensive, but the discussion needs context. Yes, training models consume significant power, and that should not be ignored. But if AI is applied to solve meaningful problems, improving weather prediction, reducing energy loss, optimising infrastructure, or addressing climate risk, the net economic and societal benefit far outweighs its carbon cost. The real issue is not that AI uses energy, but whether we use it responsibly and efficiently," stated Fawad Qureshi, Field CTO at Snowflake.

Fawad Qureshi, Field CTO, Snowflake

The concern among researchers is not simply that AI consumes energy, but rather ensuring that computational resources are used efficiently and for purposes that generate genuine societal value . India's commitment to achieving net-zero emissions by 2070 positions AI as a central pillar in that journey, with applications increasingly embedded in governance and grassroots planning .

The message emerging from both India's climate action initiatives and international discussions is clear: when technology is aligned with inclusivity and sustainability, it can become a powerful ally in the fight against climate change, particularly for vulnerable communities across the Global South .