The Global AI-Based Climate Modelling Market size is expected to reach USD1.48 billion by 2032, rising at a market growth of 20.6% CAGR during the forecast period.
Artificial Intelligence (AI) has transformed climate modelling by shifting from traditional physics-based simulations to data-driven and hybrid approaches that combine physical laws with AI’s pattern-recognition capabilities. Initially used to support scientists, AI is now central to next-generation climate models, enabling faster, more granular, and scalable predictions of extreme weather, sea-level rise, and other climate phenomena. Global government initiatives, such as Earth’s digital twins, integrate AI with satellite data, IoT sensors, and high-performance computing to simulate complex environmental systems and support policymaking. These advancements have fostered collaborations among academic institutions, public agencies, and environmental organizations to refine climate models for disaster preparedness, infrastructure planning, and regional resilience.
The AI-based climate modelling market is shaped by the rise of hybrid AI-physics techniques and large-scale foundational models trained on climate datasets, capable of tasks like long-range forecasting, high-resolution downscaling, and nowcasting. Beyond research, AI-driven forecasts are increasingly embedded into industries such as agriculture, urban planning, and logistics to assess risks, optimize operations, and build resilience against extreme weather. Public agencies are investing in AI-focused computational infrastructure, open-source ecosystems, and real-time digital platforms to democratize climate intelligence. The competitive landscape is defined by collaboration rather than rivalry, with governments, universities, and technology providers working together to develop interoperable AI systems that empower informed decision-making and climate-resilient strategies across sectors.
The major strategies followed by the market participants are Product Launches as the key developmental strategy to keep pace with the changing demands of end users. In June, 2025, NVIDIA Corporation unveiled cBottle, a generative AI model within its Earth-2 platform to create an interactive digital twin of Earth’s climate. Trained on 50 years of data, cBottle produces high-resolution, energy-efficient climate simulations to improve prediction, understanding, and response to extreme weather and climate change. Additionally, In June, 2025, Google LLC unveiled a hybrid AI-physics model that merges traditional climate simulations with generative AI to produce detailed, local climate risk forecasts at 10 km resolution. This innovative downscaling method improves accuracy, reduces computational costs by up to 85%, and supports better regional climate planning and disaster preparedness.
Based on the Analysis presented in the KBV Cardinal matrix; Google LLC, Microsoft Corporation, NVIDIA Corporation, and Amazon Web Services, Inc. are the forerunners in the AI-Based Climate Modelling Market. Companies such as IBM Corporation, ClimateAi, and Jupiter Intelligence are some of the key innovators in AI-Based Climate Modelling Market. In May, 2025, ClimateAi unveiled FICE, the world’s first AI model linking climate and economic data to predict how extreme weather impacts consumer spending and business revenue. Combining hyper-local weather insights with macroeconomic trends, FICE helps companies and governments plan for climate risks, drive resilience, and uncover growth opportunities.
The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies to cater to demand coming from the different industries. The key developmental strategies in the market are Acquisitions, Product Launches and Product Expansions, and Partnerships & Collaborations.
The Asia-Pacific and LAMEA region are also seeing the expansion of the AI-Based Climate Modelling Market because of increasing climate vulnerability and rising infrastructure investments. In Asian countries such as Japan, India, Australia, and China, AI-based modelling is being used for monsoon forecasting, agricultural optimization, flood prediction, and urban climate resilience planning. In addition to this, the requirement to address droughts, water scarcity, and heatwaves is raising the usage of AI-based climate models in the LAMEA region. Artificial intelligence is being implemented in the region for regional agricultural planning systems and disaster management. Rising collaborations between cloud-based AI platforms, global agencies, and donor-funded climate projects are supporting the expanding adoption of AI-based climate modelling and capacity building.
The AI-based climate modelling market is witnessing intensifying competition as both established technology providers and emerging innovators strive to deliver advanced solutions for accurate forecasting, risk assessment, and sustainability planning. Competition is driven by the growing demand for precise climate predictions to address extreme weather events, policy compliance, and corporate sustainability initiatives. Players are differentiating themselves through specialized AI algorithms, high-performance computing integration, and the ability to process large, complex datasets from satellites, sensors, and historical records. The market also sees rising collaboration with academic institutions and government bodies to improve model transparency and reliability. With increasing emphasis on decarbonization and ESG reporting, the competitive landscape is shaped by innovation, scalability, and the ability to provide actionable, real-time insights.
Key Highlights:
- The North America AI-Based Climate Modelling market dominated the Global Market in 2024, accounting for a 39.30% revenue share in 2024.
- The US AI-Based Climate Modelling market is expected to continue its dominance in North America region, thereby reaching a market size of 10 million by 2032.
- Among the various Application segments, the Weather Forecasting segment dominated the global market, contributing a revenue share of 68.3% in 2024.
- In terms of the Component segmentation, the Software segment is projected to dominate the global market with the projected revenue share of 61.48%m in 2032.
- Machine Learning led the Technology segments in 2024, capturing a 40.22% revenue share and is projected to continue its dominance during projected period.
Artificial Intelligence (AI) has transformed climate modelling by shifting from traditional physics-based simulations to data-driven and hybrid approaches that combine physical laws with AI’s pattern-recognition capabilities. Initially used to support scientists, AI is now central to next-generation climate models, enabling faster, more granular, and scalable predictions of extreme weather, sea-level rise, and other climate phenomena. Global government initiatives, such as Earth’s digital twins, integrate AI with satellite data, IoT sensors, and high-performance computing to simulate complex environmental systems and support policymaking. These advancements have fostered collaborations among academic institutions, public agencies, and environmental organizations to refine climate models for disaster preparedness, infrastructure planning, and regional resilience.
The AI-based climate modelling market is shaped by the rise of hybrid AI-physics techniques and large-scale foundational models trained on climate datasets, capable of tasks like long-range forecasting, high-resolution downscaling, and nowcasting. Beyond research, AI-driven forecasts are increasingly embedded into industries such as agriculture, urban planning, and logistics to assess risks, optimize operations, and build resilience against extreme weather. Public agencies are investing in AI-focused computational infrastructure, open-source ecosystems, and real-time digital platforms to democratize climate intelligence. The competitive landscape is defined by collaboration rather than rivalry, with governments, universities, and technology providers working together to develop interoperable AI systems that empower informed decision-making and climate-resilient strategies across sectors.
The major strategies followed by the market participants are Product Launches as the key developmental strategy to keep pace with the changing demands of end users. In June, 2025, NVIDIA Corporation unveiled cBottle, a generative AI model within its Earth-2 platform to create an interactive digital twin of Earth’s climate. Trained on 50 years of data, cBottle produces high-resolution, energy-efficient climate simulations to improve prediction, understanding, and response to extreme weather and climate change. Additionally, In June, 2025, Google LLC unveiled a hybrid AI-physics model that merges traditional climate simulations with generative AI to produce detailed, local climate risk forecasts at 10 km resolution. This innovative downscaling method improves accuracy, reduces computational costs by up to 85%, and supports better regional climate planning and disaster preparedness.
KBV Cardinal Matrix - Market Competition Analysis
Based on the Analysis presented in the KBV Cardinal matrix; Google LLC, Microsoft Corporation, NVIDIA Corporation, and Amazon Web Services, Inc. are the forerunners in the AI-Based Climate Modelling Market. Companies such as IBM Corporation, ClimateAi, and Jupiter Intelligence are some of the key innovators in AI-Based Climate Modelling Market. In May, 2025, ClimateAi unveiled FICE, the world’s first AI model linking climate and economic data to predict how extreme weather impacts consumer spending and business revenue. Combining hyper-local weather insights with macroeconomic trends, FICE helps companies and governments plan for climate risks, drive resilience, and uncover growth opportunities.
COVID-19 Impact Analysis
The COVID-19 pandemic messed up the AI-based climate modeling market by stopping field research, data collection, and infrastructure development because of lockdowns and travel restrictions. This made it harder to calibrate and validate models. Even though these problems arose, the crisis sped up the shift to digital technologies, with more reliance on historical datasets, satellite images, cloud computing, and machine learning platforms to keep climate simulations going from a distance. Governments and policymakers are putting more emphasis on AI-driven climate models for planning for resilience. At the same time, partnerships between public agencies, researchers, and technology providers are making open-access platforms more popular. In general, the pandemic made climate modeling stronger in terms of innovation, remote operability, and long-term adaptability, even though short-term disruptions slowed progress. In conclusion, the COVID-19 pandemic, though disruptive, accelerated digital transformation in AI-based climate modelling, enhancing long-term resilience and adaptability in global climate research.Driving and Restraining Factors
Drivers- Increasing Frequency of Extreme Weather Events Driving Demand for Accurate Predictive Models
- Advancements in High-Performance Computing and Cloud Infrastructure
- Policy Push and International Commitments on Climate Resilience and Sustainability
- Proliferation of Remote Sensing and IoT-Enabled Environmental Monitoring
- Data Quality, Standardization, and Interoperability Challenges
- High Computational Costs and Infrastructure Barriers
- Limited Domain Expertise and Integration of AI with Traditional Climate Science
- Integration of AI-Based Climate Modelling with Climate Insurance and Financial Risk Management
- Leveraging AI-Climate Models for Precision Agriculture and Food Security Planning
- Development of AI-Driven Digital Twin Ecosystems for Urban Climate Resilience
- Ensuring Scientific Transparency and Explainability of AI Models in Climate Science
- Aligning AI-Based Climate Modelling with Global Policy and Governance Frameworks
- Managing the Ethical Implications of AI-Driven Climate Modelling in Socio-Economic Decision Making
Market Share Analysis
The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies to cater to demand coming from the different industries. The key developmental strategies in the market are Acquisitions, Product Launches and Product Expansions, and Partnerships & Collaborations.
Component Outlook
Based on Component, the Global AI-Based Climate Modelling Market is segmented into Software and Services. The services segment procured 30.2% revenue share in the market in 2024. Services are vital for organizations that lack the technical expertise to deploy and operate advanced AI tools independently. Service providers assist clients in integrating modelling tools into existing systems, training personnel, and ensuring continuous system optimization. With the growing need for localized climate insights and strategic climate risk planning, demand for professional services is expected to remain robust across both public and private sector applications.Application Outlook
On the Basis of Application, the market is segmented into Weather Forecasting, Disaster Prediction, Climate Risk Assessment, Carbon Emission Tracking, and Other Application. The disaster prediction segment recorded 18.3% revenue share in the market in 2024. AI-powered climate models can identify early warning signals, simulate impact scenarios, and support emergency response planning. Governments and humanitarian organizations are increasingly adopting these tools to enhance preparedness and reduce loss of life and property during extreme weather events.
Regional Outlook
Region-wise, the market is analyzed across North America, Europe, Asia-Pacific, and LAMEA. The North America segment recorded 39.3% revenue share in the market in 2024. The AI-Based Climate Modelling Market is witnessing prominent growth in North America and the European region because of advanced research infrastructure, significant funding from governments, collaborations between universities, private tech firms, and public agencies. Market in North American countries also benefits from programs held by organizations like NOAA, NASA, AI for wildfire prediction, disaster response, and high-resolution weather forecasting. Furthermore, Europe is also having a promising rise in the market with initiatives like Destination Earth, European Green Deal that promote the development of AI-based “digital-twins” with aim of planning climate-neutral strategies. Both North America as well as Europe are witnessing high adoption in agriculture, energy, and insurance industries with a major focus on transparency, model interpretability and ethical use of AI in environmental decision-making.The Asia-Pacific and LAMEA region are also seeing the expansion of the AI-Based Climate Modelling Market because of increasing climate vulnerability and rising infrastructure investments. In Asian countries such as Japan, India, Australia, and China, AI-based modelling is being used for monsoon forecasting, agricultural optimization, flood prediction, and urban climate resilience planning. In addition to this, the requirement to address droughts, water scarcity, and heatwaves is raising the usage of AI-based climate models in the LAMEA region. Artificial intelligence is being implemented in the region for regional agricultural planning systems and disaster management. Rising collaborations between cloud-based AI platforms, global agencies, and donor-funded climate projects are supporting the expanding adoption of AI-based climate modelling and capacity building.
Market Competition and Attributes
The AI-based climate modelling market is witnessing intensifying competition as both established technology providers and emerging innovators strive to deliver advanced solutions for accurate forecasting, risk assessment, and sustainability planning. Competition is driven by the growing demand for precise climate predictions to address extreme weather events, policy compliance, and corporate sustainability initiatives. Players are differentiating themselves through specialized AI algorithms, high-performance computing integration, and the ability to process large, complex datasets from satellites, sensors, and historical records. The market also sees rising collaboration with academic institutions and government bodies to improve model transparency and reliability. With increasing emphasis on decarbonization and ESG reporting, the competitive landscape is shaped by innovation, scalability, and the ability to provide actionable, real-time insights.
Recent Strategies Deployed in the Market
- Jul-2025: Tomorrow.io teamed up with Palantir to deliver AI-driven weather forecasts for defense, government, and enterprise clients. Tomorrow.io, using AI and satellite data, joins Palantir’s FedStart program to expand within the U.S. federal sector. This collaboration enhances weather modeling for operational decisions during extreme weather events.
- May-2025: Microsoft Corporation unveiled Aurora, a powerful AI model that predicts weather, air quality, typhoons, and other atmospheric events more accurately and quickly than traditional methods. Aurora outperformed global forecasting centers in 91% of tests, delivering lifesaving, precise predictions in seconds instead of hours, revolutionizing weather forecasting.
- May-2025: ClimateAi unveiled FICE, the world’s first AI model linking climate and economic data to predict how extreme weather impacts consumer spending and business revenue. Combining hyper-local weather insights with macroeconomic trends, FICE helps companies and governments plan for climate risks, drive resilience, and uncover growth opportunities.
- Sep-2024: NVIDIA Corporation teamed up with G42 to develop advanced climate solutions using NVIDIA’s Earth-2 platform. This collaboration aims to deliver next-generation AI-powered climate modeling and digital twin technologies, helping improve prediction, preparedness, and response to climate change impacts while supporting global sustainability and resilience goals from Abu Dhabi, UAE.
- Jun-2024: Jupiter Intelligence unveiled Jupiter AI, a generative AI tool that accelerates climate risk analysis for businesses. Integrated with ClimateScore Global, it provides easy, conversational access to detailed climate impact insights on physical assets and portfolios, enabling faster, data-driven decisions to enhance climate resilience without requiring data science expertise.
- Mar-2024: NVIDIA Corporation unveiled new Earth-2 climate digital twin platforms that uses AI and generative models like CorrDiff to create ultra-high-resolution, energy-efficient weather and climate simulations. Early adopters include Taiwan’s Central Weather Administration and The Weather Company, aiming to improve disaster preparedness and global forecasting at unprecedented speed and scale.
List of Key Companies Profiled
- NVIDIA Corporation
- IBM Corporation
- Microsoft Corporation
- Google LLC
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- AccuWeather, Inc.
- ClimateAi
- Tomorrow.io
- The Climate Corporation
- Jupiter Intelligence
Market Report Segmentation
By Component
- Software
- Services
By Application
- Weather Forecasting
- Disaster Prediction
- Climate Risk Assessment
- Carbon Emission Tracking
- Other Application
By Technology
- Machine Learning
- Deep Learning
- Computer Vision
- Other Technology
By Geography
- North America
- US
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- UK
- France
- Russia
- Spain
- Italy
- Rest of Europe
- Asia-Pacific
- China
- Japan
- India
- South Korea
- Singapore
- Malaysia
- Rest of Asia-Pacific
- LAMEA
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market at a Glance
Chapter 3. Market Overview
Chapter 8. Competition Analysis - Global
Chapter 9. Value Chain Analysis of AI-Based Climate Modelling Market
Chapter 11. Global AI-Based Climate Modelling Market by Component
Chapter 12. Global AI-Based Climate Modelling Market by Application
Chapter 13. Global AI-Based Climate Modelling Market by Technology
Chapter 14. Global AI-Based Climate Modelling Market by Region
Chapter 15. Company Profiles
Companies Mentioned
- NVIDIA Corporation
- IBM Corporation
- Microsoft Corporation
- Google LLC
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- AccuWeather, Inc.
- ClimateAi
- Tomorrow.io
- The Climate Corporation
- Jupiter Intelligence
