  Recent wildfires in Australia have led to considerable economic loss and
property destruction, and there is increasing concern that climate change may
exacerbate their intensity, duration, and frequency. Hazard quantification for
extreme wildfires is an important component of wildfire management, as it
facilitates efficient resource distribution, adverse effect mitigation, and
recovery efforts. However, although extreme wildfires are typically the most
impactful, both small and moderate fires can still be devastating to local
communities and ecosystems. Therefore, it is imperative to develop robust
statistical methods to reliably model the full distribution of wildfire spread.
We do so for a novel dataset of Australian wildfires from 1999 to 2019, and
analyse monthly spread over areas approximately corresponding to Statistical
Areas Level~1 and~2 (SA1/SA2) regions. Given the complex nature of wildfire
ignition and spread, we exploit recent advances in statistical deep learning
and extreme value theory to construct a parametric regression model using graph
convolutional neural networks and the extended generalized Pareto distribution,
which allows us to model wildfire spread observed on an irregular spatial
domain. We highlight the efficacy of our newly proposed model and perform a
wildfire hazard assessment for Australia and population-dense communities,
namely Tasmania, Sydney, Melbourne, and Perth.
