Climate X, a provider of climate resilience analytics, announced the launch of Global Wildfire, a global probabilistic wildfire model designed to help organizations assess and manage wildfire risk at the asset level.
The increasing frequency and severity of wildfires creates challenges for organizations seeking accurate and consistent risk information, the company said. Climate X said Swiss Re estimates wildfire losses could reach $40 billion in 2025, and many existing models fail to provide the level of detail needed to assess risks in a globally diversified portfolio.
Global Wildfire was developed to provide insurers, asset managers and public sector organizations with detailed insights into wildfire exposure, allowing them to assess potential impacts and make informed decisions.
“Wildfires are a significant risk management issue,” added Lukky Ahmed, co-founder and CEO of Climate
According to Climate X, the model provides a more detailed alternative to index-based and historical wildfire assessments by combining scientific modeling techniques with asset-level analysis. Global Wildfire provides risk output at 30m (98ft) resolution, incorporating factors such as local landscape conditions, architectural features and potential financial impact, the company said.
The model can also be used to evaluate wildfire scenarios under different climate pathways into the year 2100, helping organizations understand how exposure may develop under changing climate conditions.
“Global Wildfire is an important step beyond index-led or purely historical approaches to wildfire risk,” Ahmed added. “We offer clients a way to move from risk insights to true financial risk quantification, gaining the more transparent, scientifically sound view they need for underwriting, credit risk and regulatory scenario analysis.”
Global Wildfires covers 93% of global GDP in eight regions, enabling financial institutions to compare wildfire risk across multiple markets using a consistent methodology. The company emphasized that many existing wildfire models have historically focused on specific high-risk areas, including the United States and Australia, while global organizations often need to conduct comparable risk assessments across a wider range of locations.
The model generates absolute probabilistic burn probabilities rather than relative risk scores or qualitative rankings. The company said this provides organizations with measurable risk estimates and uncertainty bounds that can support investment reviews, portfolio analysis, stress testing, governance processes and financial modelling.
Global Wildfire uses machine learning trained on satellite data to assess local fire susceptibility, including factors such as topography, vegetation, and human impact within the wildland-urban interface. Climate X said it then used cellular automata methods to combine ignition risk with physical fire spread simulations to model thousands of potential wildfire events per site.
These simulations account for changes in ignition point, fire size and direction of spread, providing organizations with a detailed view of potential wildfire impacts on their assets and portfolios.
