AI-Based Climate Modelling Market Size - By Component, By Deployment Mode, By Technology, By Application Analysis, Share, Growth Forecast, 2025 - 2034

AI-Based Climate Modelling Market Size

The global AI-based climate modelling market size was valued at USD 266.4 million in 2024 and is projected to grow at a CAGR of 23.1% between 2025 and 2034, driven by the growing awareness of climate change and its devastating impacts, prompting the need for advanced tools to predict and mitigate risks effectively. Rapid advancements in AI technologies, such as machine learning and deep learning, have enabled more accurate, granular, and real-time climate predictions.
 

Governments and organizations are increasing investments in climate resilience initiatives, while regulatory pressures on climate-related disclosures and sustainability practices are fostering demand for sophisticated AI tools. Moreover, the growing availability of high-quality climate data and integration with other technologies like IoT and cloud computing further enhances the applicability of AI models. This market is also propelled by industries such as energy, agriculture, and insurance seeking data-driven strategies for risk assessment and resource management?.
 

For instance, in April 2023, ClimateAI, a pioneering start-up in AI-driven climate change mitigation, raised USD 22 million in Series B funding in 2023. Its platform utilizes AI for climate risk modeling, offering long-term forecasts and risk assessments tailored to crop yields and supply chains. These insights enable businesses to take proactive steps to adapt to future climate disruptions effectively. This innovation highlights AI’s potential to address pressing climate challenges.
 

Another growth driver of the AI-based climate modelling market is the increasing need for predictive tools in disaster risk management. With the frequency of extreme weather events rising due to climate change, governments, and organizations are leveraging AI-driven models for early warning systems and preparedness planning. These tools enhance the ability to minimize economic losses and protect vulnerable populations. This growing reliance on AI for real-time and long-term disaster resilience is boosting market demand?.
 

AI-Based Climate Modelling Market Trends

An ongoing trend in the AI-based climate modelling industry is the integration of AI with advanced data ecosystems, such as IoT, blockchain, and cloud computing. These technologies enable real-time monitoring and detailed climate analysis, making predictions more actionable and precise. There is also a growing emphasis on hyper-localized forecasts, helping industries like agriculture and logistics optimize their operations. Additionally, the use of generative AI and deep learning is advancing climate scenario simulations, offering insights into long-term climate risks and their cascading effects. This trend reflects the push for more accessible, scalable, and reliable tools for climate resilience across sectors??.
 

For instance, in September 2024, Fermata’s AI-powered platform, Croptimus, revolutionizes farming by using machine learning and computer vision to combat pests and crop diseases in real-time. The system provides 24/7 monitoring with cameras mounted on greenhouses, drones, or robots, offering real-time analytics and detailed maps for targeted interventions. Croptimus minimizes crop losses, reduces pesticide use, and enhances sustainability while optimizing human labor. Trained on high-quality data and powered by NVIDIA technology, it augments traditional farming workflows without replacing them. This innovation helps farmers improve yields, cut costs, and reduce environmental impact in a low-margin, resource-intensive industry.
 

One challenge in the AI-based climate modelling market is the inherent complexity and uncertainty of long-term climate predictions. AI models rely on large datasets, but gaps in data availability—especially in developing regions—can limit accuracy and robustness. Moreover, integrating diverse datasets from multiple sources, such as satellite imagery, weather reports, and historical records, poses technical challenges. The high computational costs of training and deploying sophisticated AI models further add to implementation barriers. These factors make it difficult for organizations to adopt and scale AI solutions effectively across all regions.
 

AI-Based Climate Modelling Market Analysis

Based on the component, the market is segmented into software and services. In 2024, the software segment accounted for over 80% of the market share and is expected to exceed USD 1.4 billion by 2034. The software segment dominates the market due to its versatility and scalability across industries.
 

Advanced algorithms and machine learning frameworks enable software solutions to process and analyze vast amounts of climate data, generating accurate forecasts and actionable insights. These tools are widely adopted in sectors like agriculture, energy, and disaster management for real-time decision-making. Additionally, cloud-based deployment models make software more accessible, reducing infrastructure costs and enabling global reach. Regular updates and customizability also give software solutions a competitive edge, addressing diverse needs from hyper-local predictions to large-scale climate risk assessments. This adaptability drives strong demand and positions software as the leading market segment.
 

Based on the application, the AI-based climate modelling market is divided into weather forecasting, climate prediction, disaster risk reduction, environmental monitoring and others. The weather forecasting segment held above 45% of the market share in 2024, due to the critical need for accurate, real-time weather predictions in various sectors like agriculture, energy, and transportation.
 

AI models can analyze vast datasets, including satellite imagery and meteorological reports, to provide more precise forecasts, enabling businesses to plan better and mitigate risks. This capability is essential for managing natural disasters, reducing operational downtime, and optimizing energy consumption. Additionally, advancements in machine learning and cloud computing have enhanced the scalability and accessibility of weather forecasting solutions. These factors make weather forecasting a key driver in the growth of the market.
 

The U.S. AI-based climate modelling market accounted for 80% of the revenue share in 2024 and is expected to exceed USD 500 million by 2034, due to its strong technological infrastructure and leadership in AI research and development. The presence of major technology companies and startups, along with substantial investments in AI and climate-related initiatives, fuels innovation in climate modelling tools.
 

Furthermore, the U.S. government’s focus on climate resilience, disaster management, and environmental sustainability drives demand for advanced forecasting systems. Additionally, the large-scale adoption of AI solutions in sectors such as agriculture, energy, and insurance further accelerate market growth. These factors combine to position the U.S. as a key player in the global market.
 

The Asia Pacific region is rapidly expanding in the AI-based climate modelling market, driven by growing concerns over climate change and extreme weather events. Countries like China, Japan, and India are increasingly adopting AI technologies to enhance weather forecasting, disaster management, and agricultural practices.
 

Government initiatives focused on climate resilience, along with investments in AI research, are propelling market growth. The region also benefits from technological advancements in machine learning and cloud computing, which are making AI-based solutions more accessible. However, the challenge remains in data accessibility and the need for localized models tailored to diverse regional needs.
 

Europe is a key player in the AI-based climate modelling market, with a strong focus on sustainability and regulatory frameworks. The European Union's green initiatives and policies, such as the European Green Deal, drive demand for climate resilience solutions, encouraging the adoption of AI for climate predictions and mitigation strategies.
 

Countries like Germany, France, and the U.K. are investing heavily in AI-driven climate forecasting tools for sectors like agriculture, energy, and disaster management. The region’s advanced technological infrastructure and commitment to reducing carbon emissions further fuel market growth. Data privacy regulations and the need for localized solutions present challenges in certain areas, but Europe remains a leading market?.
 

AI-Based Climate Modelling Market Share

Google LLC, AccuWeather, and Microsoft collectively held a substantial market share of over 31% in the AI-based climate modelling industry in 2024. Google LLC, AccuWeather, and Microsoft Corporation dominate the AI-based climate modelling industry due to their substantial technological expertise, infrastructure, and global reach. Google leverages its advanced AI and cloud computing platforms, like Google Cloud and TensorFlow, to provide scalable climate models and weather prediction services. AccuWeather's long-standing position in weather forecasting, combined with AI tools, enables it to offer highly localized and real-time weather solutions.
 

Microsoft, with its Azure cloud platform and AI-driven analytics, powers climate resilience and forecasting models for various sectors. These companies also benefit from extensive data networks, strong R&D capabilities, and collaborations with governments and industries, making them leaders in the market. Their significant investments in AI and cloud technologies, alongside vast computational power, give them a competitive edge in providing reliable and efficient climate modelling solutions?.
 

AI-Based Climate Modelling Market Companies

Major players operating in the AI-based climate modelling industry are:

• AccuWeather
• Arundo Analytics
• Atmos AI
• ClimateAI
• Google
• IBM
• Jupiter Intelligence
• Microsoft Corporation
• Open Climate Fix
• Weather Technologies
   

AI-Based Climate Modelling Industry News

• In September 2024, Fermata’s AI-powered platform, Croptimus, revolutionizes farming by using machine learning and computer vision to combat pests and crop diseases in real-time. The system provides 24/7 monitoring with cameras mounted on greenhouses, drones, or robots, offering real-time analytics and detailed maps for targeted interventions. Croptimus minimizes crop losses, reduces pesticide use, and enhances sustainability while optimizing human labor. Trained on high-quality data and powered by NVIDIA technology, it augments traditional farming workflows without replacing them. This innovation helps farmers improve yields, cut costs, and reduce environmental impact in a low-margin, resource-intensive industry.
   
• In April 2023, ClimateAI, a pioneering start-up in AI-driven climate change mitigation, raised USD 22 million in Series B funding in 2023. Its platform utilizes AI for climate risk modeling, offering long-term forecasts and risk assessments tailored to crop yields and supply chains. These insights enable businesses to take proactive steps to adapt to future climate disruptions effectively. This innovation highlights AI’s potential to address pressing climate challenges.
   

The AI-based climate modelling market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue ($ Mn/Bn) from 2021 to 2034, for the following segments:

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Market, By Component

• Software
• Services

Market, By Deployment Mode

• On-premises
• Cloud

Market, By Technology

• Machine learning
• Deep learning
• Natural Language Processing (NLP)
• Computer vision
• Others

Market, By Application

• Weather forecasting
• Climate prediction
• Disaster risk reduction
• Environmental monitoring
• Others

The above information is provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Italy
  • Spain
  • Russia
  • Nordics
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • ANZ
  • Southeast Asia
• Latin America
  • Brazil
  • Mexico
  • Argentina
• MEA
  • UAE
  • Saudi Arabia
  • South Africa