Probabilistic forecasting of wildfire ignitions and intensity at sub-kilometre scale using diffusion models

Wildfires are becoming more frequent and severe in many fire-prone regions, with disproportionate impacts on carbon emissions, ecosystems, and society. However, existing fire and Earth system models still struggle to represent the highly localized and stochastic nature of extreme fire ignitions and to quantify their short-term impacts at fine spatial scales.

In this work, we develop a data-driven framework for next day active fire forecasting at sub-kilometre resolution by combining reanalysis meteorology with satellite fire observations. Our approach builds on recent advances in spatio-temporal deep learning from the AI community, in particular Earth system transformers and denoising diffusion probabilistic models. We use multi-year ERA5 meteorological fields together with static variables (topography, land cover, fuel proxies) as training data, and VIIRS active fire detections and pixel-level brightness/fire radiative power as targets.

The model consists of a deterministic spatio-temporal backbone that encodes the joint evolution of weather and surface conditions, coupled to a diffusion-based probabilistic head that predicts the distribution of future ignition locations and associated fire intensity. This design allows us to explicitly represent uncertainty in rare, extreme events while retaining high spatial resolution. We evaluate the system on multiple fire-prone regions and held-out seasons containing documented extreme fire episodes. Preliminary results show improved skill in localizing ignitions and capturing extreme-tail intensity compared to baseline statistical and convolutional models, particularly in top-k precision metrics relevant for operational targeting.

We plan to couple the predicted intensity fields with standard emission factors to estimate event-scale CO₂ emissions and explore the relative importance of meteorological and surface drivers using feature attribution techniques using causality discovery methods. Our findings illustrate the potential of modern probabilistic deep learning to bridge between high-resolution fire observations and Earth system applications, and to support the assessment and management of future extreme fires.