Wildfires pose a growing challenge for weather and climate science, with major impacts on ecosystems, air quality, infrastructure, and human safety. Climate change is increasing wildfire risk through rising temperatures, more frequent droughts, shifting precipitation patterns, and changing wind and humidity conditions. As a result, extreme wildfires are becoming more widespread and frequent, shifting from occasional hazards to a persistent feature of the Earth system.

Advances in field observations, remote sensing, reanalysis products, and high-resolution modelling now provide unprecedented access to meteorological and environmental data. These datasets create new opportunities to study wildfires. A key challenge is combining these diverse data sources into modelling frameworks that support reliable wildfire prediction. Turning data into skilful, actionable forecasts requires ongoing innovation in numerical modelling, statistical methods, and machine learning.

Extreme wildfire events deserve special focus. In these cases, large fire plumes generate their own weather and alter the atmospheric boundary layer. The formation of pyrocumulus and pyrocumulonimbus clouds can dramatically accelerate fire spread. Such fires exhibit new behaviour that we are only beginning to understand and that is not yet captured well in fire spread models.

This session brings together researchers and fire analysts working at the intersection of weather, climate, and wildfire science. We encourage contributions from communities in high-resolution modelling, numerical weather prediction, climate modelling, Earth observation, and climate services, with a focus on understanding fundamental physics, as well as improving how fire-relevant processes and uncertainties are represented in forecasts and projections.

Topics include, but are not limited to:
• Observational and modelling approaches to extreme wildfire dynamics and
• pyroconvective fire spread
• Fire-weather and fire-climate relationships, including extremes and compound events
• Statistical, dynamical, and machine-learning methods for predicting wildfire occurrence and spread
• Integration of satellite data, reanalysis, and in situ observations into forecasting
• frameworks
• Uncertainty assessment of wildfire-relevant variables in weather and climate models
• Transdisciplinary research involving collaboration with operational firefighters
• Applications in climate services, early-warning systems, and risk-based decision support