Global AI-Based Climate Modelling Market Size 2025 - 2035

Global AI-Based Climate Modelling Market Insights Forecasts to 2035

• The Global AI-Based Climate Modelling Market Size Was Estimated at USD 342.6 Million in 2024
• The Market Size is Expected to Grow at a CAGR of around 21.77% from 2025 to 2035
• The Worldwide AI-Based Climate Modelling Market Size is Expected to Reach USD 2991.7 Million by 2035
• Asia Pacific is expected to grow the fastest during the forecast period.

AI-Based Climate Modelling Market

AI-based climate modelling leverages artificial intelligence, including machine learning and deep learning, to simulate and predict climate patterns with improved accuracy and speed. It processes vast datasets from satellites, sensors, and climate systems to support real-time forecasting, long-term climate projections, and disaster risk assessment. This technology is transforming traditional climate science by enabling more precise environmental modelling and early warning systems. Governments across the globe are actively promoting AI integration in climate modelling through strategic initiatives. For instance, the United States National Oceanic and Atmospheric Administration (NOAA) has invested in AI to enhance weather and climate forecasts, while the European Union has launched the Destination Earth program to develop digital twins of the planet. Similarly, countries like India and Japan are adopting AI in meteorological services to improve resilience against climate-related events. These efforts are fostering innovation, enhancing data-driven policymaking, and positioning AI as a crucial tool in addressing global climate challenges.

Attractive Opportunities in the AI-Based Climate Modelling Market

• Creating open-access platforms to democratize climate intelligence, allowing developing nations and smaller institutions to benefit from advanced AI modelling tools, fostering innovation and wider adoption.
• Transforming complex climate data into intuitive, easy-to-understand visual formats that help policymakers and the public grasp climate risks better, driving more informed decision-making and engagement.
• Leveraging AI for climate-resilient crop planning, risk-based insurance pricing, and infrastructure adaptation, opening new markets and applications beyond traditional forecasting and risk assessment.

Global AI-Based Climate Modelling Market Dynamics

DRIVER: Urgent global need for predictive tools that can provide early warnings for climate-related disasters such as droughts

The growth of the AI-based climate modelling market is being fueled by several unique and transformative factors. One key driver is the urgent global need for predictive tools that can provide early warnings for climate-related disasters such as droughts, cyclones, and heatwaves. AI offers the ability to process and learn from massive volumes of environmental data in real time, far surpassing the limitations of traditional models. The rapid advancement of satellite imaging, remote sensing technologies, and IoT-based climate monitoring is providing AI systems with increasingly rich datasets. Moreover, breakthroughs in neural networks and edge computing are enabling more localized and high-resolution climate forecasts. Government-backed initiatives focused on climate resilience are also accelerating AI adoption, with funding directed toward innovative modelling platforms. Additionally, as climate change becomes central to global policy discussions, industries and research institutions are turning to AI to support sustainability goals, carbon tracking, and climate impact assessments, fueling steady market expansion.

RESTRAINT: High cost of deploying sophisticated AI systems

One of the most significant is the high cost of deploying sophisticated AI systems, which demand powerful computing infrastructure and large-scale data management resources often out of reach for many institutions, especially in low-income regions. Another challenge lies in data availability and consistency; many parts of the world lack comprehensive historical climate records, limiting the effectiveness of AI training and prediction accuracy. There's also a critical skills gap: professionals who understand both advanced AI and climate science are in short supply, making interdisciplinary collaboration difficult. Additionally, concerns about data governance, including privacy and ethical use of satellite or geospatial data, can restrict data sharing and integration. Integration hurdles with legacy climate models and resistance to new, unfamiliar technologies further slow adoption. These factors together create barriers that must be addressed to fully realize AI’s potential in climate science.

OPPORTUNITY: Collaboration between public agencies

Collaboration between public agencies, academia, and private tech firms can lead to open-access platforms that democratize climate intelligence, allowing developing nations and smaller institutions to benefit from advanced modelling tools. Additionally, AI can enhance climate education and awareness by transforming complex data into intuitive visualizations, helping policymakers and the public better understand climate risks. There's also potential for AI to uncover new climate patterns and anomalies previously undetectable through conventional models, offering deeper insights into long-term changes. Integrating AI into insurance, agriculture, and infrastructure sectors opens new commercial applications, from climate-resilient crop planning to risk-based pricing models. These opportunities signal AI’s growing role not just in prediction, but in proactive climate adaptation and decision-making.

CHALLENGES: Ensuring the interpretability and transparency of AI models

Ensuring the interpretability and transparency of AI models, as complex algorithms often function as “black boxes,” making it difficult for scientists and policymakers to fully trust or understand the decision-making process. Another challenge involves managing the ethical implications of AI predictions, including potential biases in data that could lead to unfair or inaccurate outcomes affecting vulnerable communities. Additionally, continuously updating AI models to incorporate new climate data and evolving environmental conditions requires ongoing maintenance and expertise. There is also the challenge of fostering international cooperation and data sharing across borders, which is essential for creating comprehensive global climate models but often hindered by geopolitical and legal constraints. Lastly, ensuring that AI climate solutions are inclusive and accessible to underrepresented regions remains a hurdle, limiting equitable benefits from advanced technology. These challenges highlight the complexity of integrating AI into climate science responsibly and effectively.

Global AI-Based Climate Modelling Market Ecosystem Analysis

The global AI-based climate modelling market ecosystem involves AI technology developers, cloud computing providers, and data sources like satellite agencies and meteorological organizations. Research institutions and governments collaborate to advance climate science and fund initiatives. End users from sectors such as agriculture, disaster management, energy, and insurance apply AI insights for better decision-making. Software vendors and consulting firms support model development and integration. Public-private partnerships and open data initiatives drive innovation and data sharing, creating a collaborative environment for AI-powered climate solutions worldwide.

Based on the technology, the machine learning segment accounted for the highest revenue share of the AI-based climate modelling industry over the forecast period

The machine learning segment accounted for the highest revenue share in the AI-based climate modelling industry over the forecast period. This dominance is due to machine learning’s ability to analyse vast and complex climate datasets, improving prediction accuracy and enabling real-time climate simulations. Its versatility allows for applications across weather forecasting, disaster management, and long-term climate projections. As a result, machine learning remains the preferred technology within AI-driven climate modelling, driving significant revenue growth and innovation in the market.

Based on the application, the weather forecasting segment accounted for the largest revenue share of the AI-based climate modelling industry during the forecast period

The weather forecasting segment accounted for the largest revenue share of the AI-based climate modelling industry during the forecast period. This is because accurate and timely weather predictions are critical for disaster preparedness, agriculture, transportation, and public safety. AI-powered models enhance forecasting precision by processing vast amounts of meteorological data rapidly, enabling better short-term and seasonal forecasts. The high demand for improved weather services across various sectors drives the dominant revenue contribution of this segment in the AI-based climate modelling market.

North America is anticipated to hold the largest market share of the AI-based climate modelling market during the forecast period

North America is anticipated to hold the largest market share of the AI-based climate modelling market during the forecast period. This leadership is driven by substantial investments in AI research and development, strong technological infrastructure, and proactive government initiatives focused on climate resilience and disaster management. The United States, in particular, plays a central role with its advanced research institutions and major technology companies driving innovation. Collaborative efforts between public agencies and private firms further strengthen the region’s position. These factors collectively contribute to North America’s dominance in adopting and expanding AI-driven climate modelling solutions, fostering large-scale implementation and ongoing advancements in the field.

Asia Pacific is expected to grow at the fastest CAGR in the AI-based climate modelling market during the forecast period

Asia Pacific is expected to experience the fastest growth in the AI-based climate modelling market during the forecast period. This rapid expansion is attributed to increasing environmental concerns, significant investments in AI research, and the adoption of AI technologies in countries like China, India, and Japan. Governments in the region are actively promoting AI adoption to address climate challenges such as pollution, resource management, and climate change. Additionally, advancements in AI infrastructure and the growing availability of data are fueling market growth, positioning Asia Pacific as a key emerging player in AI-driven climate solutions.

Recent Development

• In December 2024, Google DeepMind introduced GenCast, a high-resolution AI ensemble model capable of forecasting weather up to 15 days in advance. Trained on four decades of ECMWF data, GenCast demonstrated superior accuracy, outperforming the ECMWF's ENS model on 97.2% of 1,320 evaluated targets. It can generate forecasts in just eight minutes using a single Google Cloud TPU v5, marking a significant leap in forecasting efficiency and precision.
• In November 2023, Microsoft unveiled Aurora, a 1.3 million parameter foundation model designed for high-resolution atmospheric forecasting. Aurora employs a flexible 3D Swin Transformer architecture, enabling it to process diverse atmospheric datasets and provide accurate weather predictions. This model aims to enhance the forecasting of extreme weather events and improve our understanding of atmospheric processes.

Key Market Players

KEY PLAYERS IN THE AI-BASED CLIMATE MODELLING MARKET INCLUDE

• IBM Corporation
• Microsoft Corporation
• Google LLC
• Amazon Web Services (AWS)
• NVIDIA Corporation
• AccuWeather, Inc.
• ClimateAi
• Jupiter Intelligence
• Atmos AI
• Open Climate Fix
• Tomorrow.io
• Arundo Analytics

• Others

Market Segment

This study forecasts revenue at global, regional, and country levels from 2020 to 2035. Spherical Insights has segmented the AI-based climate modelling market based on the below-mentioned segments: 

Global AI-Based Climate Modelling Market, By Technology

• Machine Learning
• Deep Learning
• Computer Vision

Global AI-Based Climate Modelling Market, By Application

• Weather Forecasting
• Disaster Prediction
• Climate Risk Assessment
• Carbon Emission Tracking

Global AI-Based Climate Modelling Market, By Regional Analysis

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • France
  • Italy
  • Spain
  • Russia
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Rest of Asia Pacific
• South America
  • Brazil
  • Argentina
  • Rest of South America
• Middle East & Africa
  • UAE
  • Saudi Arabia
  • Qatar
  • South Africa
  • Rest of the Middle East & Africa