AI-Based Climate Modelling Market Report Scope & Overview:

The AI-Based Climate Modelling Market was valued at USD 242.0 million in 2023 and is expected to reach USD 1715.2 million by 2032, growing at a CAGR of 34.32% from 2024-2032.

In 2023, the AI-based climate Modelling Market witnessed significant trends in model accuracy enhancement, with deep learning and physics-AI hybrid models improving long-range prediction reliability and spatial resolution. Adoption expanded notably across industries such as energy, agriculture, and insurance, while research institutes remained early adopters for emission forecasting and climate resilience projects. There was also increased integration of AI models with diverse environmental data sources like satellite imagery, sensor networks, and oceanographic feeds, enabling more dynamic and adaptive forecasts. These advancements began directly influencing climate risk assessment frameworks and policy decisions, offering localised, actionable insights for disaster preparedness, urban planning, and corporate ESG reporting. Additionally, the market is seeing new developments in AI-powered decarbonization analytics, explainable AI for climate models, regional climate AI startup ecosystems, and AI's growing role in financial climate risk management and regulatory compliance.

​In 2023, the U.S. AI-based climate modelling market was valued at approximately USD 36.3 million. It is projected to reach USD 275.2 million by 2034, growing at a CAGR of 25.25% from 2024 to 2032. Key growth drivers include increasing frequency of extreme weather events, advancements in AI and machine learning technologies, and the growing need for improved disaster response and climate change mitigation strategies.

AI-Based Climate Modelling Market Dynamics

Driver

• The rising frequency of extreme weather events drives the demand for accurate AI-based climate models for better disaster preparedness and response.

Demand for more precise climate models is increasing due to the occurrence of more extreme weather events such as hurricanes, droughts, floods, and wildfires. With the help of AI-driven models, we can get more accurate, localized, and clear predictions, enabling better disaster preparedness and response. Through these models, governments and businesses are in a much better position to anticipate the expected climate impacts, which ultimately makes it easier to develop adaptive measures. The increasing severity and uncertainty of weather phenomena are one of the reasons why the demand for real-time forecasting is increasing, further propelling the growth of the market.

Restraint

• The accuracy of AI-based climate models is limited by the availability and quality of consistent environmental data, hindering their effectiveness.

Climate models based on AI make accurate and high-quality data the key to making them reliable. The fact that you have access to consistent environmental data is a major issue. The quality of the model might be affected by the quality of the data, especially in underdeveloped regions or in hard-to-reach areas where data sets might be incomplete or sparse. Moreover, the sharing of data across sectors is often limited by fear of data privacy and regulations. This dependence on varied data sets takes significant infrastructure and makes it increasingly complex to develop & launch climate models.

Opportunity

• Governments' increasing focus on sustainability and environmental policies presents opportunities for AI-based climate models to gain funding and expand adoption.

Governments around the world are starting to recognize the role AI can play in climate science, as evident from the growing need for AI in climate initiatives, such as the U.S Green New Deal, or the European Green Deal. These policies create incentives to invest in AI-based climate modeling to improve forecasting of disasters, risk assessment, and carbon reduction strategies. The climate-based AI modeling market experiences positive growth due to augmented government funding and tie-ups with the private sector, which promote innovation, adoption, and enhanced utilization across sectors like energy, agriculture, and insurance.

Challenge

• AI-based climate models face challenges in trust and adoption due to their complex, "black box" nature, making them hard to interpret and explain.

While AI-based climate models offer significant advancements in accuracy, their complexity can hinder decision-makers' ability to understand and trust predictions. The "black box" nature of many AI systems, where the decision-making process is not easily transparent, poses a challenge in high-stakes areas like policy-making and disaster response. Ensuring that climate models are interpretable, understandable, and explainable is crucial for widespread adoption. Lack of trust in model outputs may delay decision-making processes, hindering timely climate actions.

AI-Based Climate Modelling Market Segmentation Analysis

By Component

The software segment dominated the AI-based climate modelling market in 2023 and accounted for 82% of revenue share, due to the rising demand for advanced, AI-powered modelling tools capable of providing real-time, high-resolution climate forecasts. Increased adoption by research institutions, environmental agencies, and enterprises for emission tracking, disaster management, and risk assessment boosted growth. The ability of AI software to integrate vast environmental datasets and offer scalable, cloud-based solutions continues to drive its dominance. This segment is expected to maintain its lead through 2032 with consistent advancements in AI and machine learning capabilities.

The services segment is projected to register the fastest CAGR in the AI-based climate modelling market through 2032, driven by the growing need for model customization, data integration, consulting, and system maintenance. As organizations seek to deploy complex AI climate models tailored to their operational needs, demand for professional and managed services has surged. Additionally, rising climate-related regulatory requirements and corporate ESG commitments have increased the need for specialized advisory and analytics services. This trend positions the services segment for robust, long-term growth in both the public and private sectors.

By Deployment

The cloud segment dominated the AI-based climate modelling market in 2023 and accounted for 65% of revenue share, driven by its scalability, accessibility, and ability to process massive environmental datasets in real time. Cloud-based platforms enable faster deployment, integration of multi-source data, and collaborative research, making them ideal for government agencies, research institutions, and climate-tech companies. The growing reliance on cloud infrastructure for global climate simulations and remote analytics continues to support its lead. This segment is expected to sustain steady growth through 2032, supported by advancements in AI, IoT, and satellite data integration.

The on-premises segment is expected to register the fastest CAGR through 2032, as industries with stringent data security, regulatory compliance, and infrastructure control needs increasingly adopt localized AI-based climate modelling solutions. Sectors such as defense, energy, and national research agencies prefer on-premises deployment to maintain data sovereignty and ensure uninterrupted operations in sensitive environments. Additionally, the rise in customized climate analytics for region-specific or high-resolution forecasting is driving demand for dedicated, in-house AI infrastructure, positioning the on-premises segment for strong, targeted growth over the forecast period.

By Technology

The machine learning segment dominated the AI-based climate modelling market in 2023 and accounted for significant revenue share, owing to its capability to analyze complex, multivariate environmental data and improve climate prediction accuracy. ML models are widely adopted for temperature, precipitation, and disaster pattern forecasting, supported by increasing investments from governments and research institutes. Their flexibility to handle structured and unstructured climate data makes them ideal for diverse applications. This segment is expected to maintain its dominance through 2032, driven by advancements in supervised, unsupervised, and reinforcement learning algorithms for climate simulation.

The deep learning segment is anticipated to register the fastest CAGR through 2032, as it excels at managing high-dimensional, non-linear climate data for precise, long-range predictions. Its superior capability in identifying hidden patterns and relationships in satellite images, sensor feeds, and historical weather records is fueling adoption. Deep learning’s application in next-generation climate risk modelling, oceanographic simulations, and emission scenario analysis is expanding rapidly. Increasing computational power and open-source model availability further accelerate growth, positioning deep learning as a transformative technology in the AI-based climate modelling market in the coming years.

By Application

The weather forecasting segment dominated the AI-based climate modelling market in 2023 and accounted for significant revenue share, driven by increasing demand for precise, short- and medium-term forecasts across industries like agriculture, transportation, and energy. AI-powered models enable rapid analysis of vast meteorological data, enhancing accuracy and decision-making for weather-dependent operations. Governments and private agencies are prioritizing advanced forecasting systems to mitigate economic and environmental risks. This segment is expected to maintain its lead through 2032, supported by continuous improvements in AI algorithms, IoT sensor networks, and real-time satellite data integration.

The disaster risk reduction segment is projected to record the fastest CAGR through 2032, fueled by the escalating frequency and severity of natural disasters globally. AI-based climate models are increasingly adopted for early warning systems, impact analysis, and emergency preparedness, helping governments and organizations minimize damage and casualties. Rising regulatory mandates and global climate resilience initiatives further support this segment’s growth. Additionally, AI’s ability to provide hyper-localized, predictive insights for floods, wildfires, hurricanes, and droughts is accelerating its adoption in vulnerable, disaster-prone regions worldwide.

Regional Landscape

North America dominated the AI-based climate modelling market in 2023 and represented 36% of revenue share, owing to strong government initiatives, substantial R&D investments, and the presence of major AI and climate-tech companies. The U.S. leads with federal climate action plans, funding for AI-driven research, and partnerships between universities, tech firms, and space agencies like NASA and NOAA. The region’s advanced digital infrastructure and adoption of cloud-based climate solutions across sectors such as agriculture, energy, and insurance continue to fuel growth. North America is expected to maintain its leadership position through 2032.

Asia Pacific is anticipated to register the fastest CAGR in the AI-based climate modelling market through 2032, driven by rising climate vulnerabilities and increasing adoption of AI in disaster risk management. Countries like China, India, and Japan are heavily investing in AI-powered forecasting systems to address the growing impact of typhoons, floods, and heat waves. Government-backed smart city and environmental monitoring initiatives are further accelerating demand. Additionally, rapid infrastructure digitization, expanding 5G networks, and increased collaborations with international climate research bodies position the region for robust, sustained market growth.

Key Players

The major key players, along with their products, are

• IBM — IBM Environmental Intelligence Suite

• Microsoft — Microsoft Planetary Computer

• Google — Google Earth Engine

• The Climate Corporation (Bayer) — Climate FieldView

• Accenture — Climate Analytics Platform

• AWS (Amazon Web Services) — Amazon Sustainability Data Initiative (ASDI)

• Tomorrow.io — Tomorrow.io Weather Engine

• Oracle — Oracle Climate Change Analytics

• Climavision — Climavision Climate Data Services

• Planet Labs — PlanetScope

• Descartes Labs — Descartes Labs Platform

• Cervest — EarthScan

• Jupiter Intelligence — ClimateScore Global

• One Concern — Domino Climate Platform

• ClimateAi — ClimateAi Analytics

Recent Developments

• January 2025: Secured a $230 million contract to build high-resolution Pelican satellites for an Asia-Pacific partner, marking its largest deal to date and expanding into dedicated satellite services. ​

• January 2024: The Climate Corporation introduced enhanced features in FieldView Plus and launched a new Premium subscription tier, offering personalized reports and improved in-cab experiences.