  With fires becoming increasingly frequent and severe across the globe in
recent years, understanding climate change's role in fire behavior is critical
for quantifying current and future fire risk. However, global climate models
typically simulate fire behavior at spatial scales too coarse for local risk
assessments. Therefore, we propose a novel approach towards super-resolution
(SR) enhancement of fire risk exposure maps that incorporates not only 2000 to
2020 monthly satellite observations of active fires but also local information
on land cover and temperature. Inspired by SR architectures, we propose an
efficient deep learning model trained for SR on fire risk exposure maps. We
evaluate this model on resolution enhancement and find it outperforms standard
image interpolation techniques at both 4x and 8x enhancement while having
comparable performance at 2x enhancement. We then demonstrate the
generalizability of this SR model over northern California and New South Wales,
Australia. We conclude with a discussion and application of our proposed model
to climate model simulations of fire risk in 2040 and 2100, illustrating the
potential for SR enhancement of fire risk maps from the latest state-of-the-art
climate models.
