Background

Highlights

• Introduced an integrated housing-economic recovery framework that links post-wildfire housing stability to local employment conditions and economic diversity.
• Demonstrated how traditional vulnerability tools like SoVI overlook hidden and dynamic vulnerabilities, especially among renters, seasonal workers, and undocumented residents.

The North American boreal biome (NAB) is warming at 2–4 times the mean global rate, contributing to increasing wildfire activity. The degree to which this trend alters biome-level feedbacks to global climate depends on how strongly bottom-up feedbacks between fire and vegetation dampen the effects of climate drivers.

Accurate fire weather forecasting is essential for effective wildfire management, particularly in regions increasingly affected by extreme fire activity such as British Columbia and Alberta, Canada.

Rising temperatures may disrupt reproduction before becoming lethal; thus mating traits could define species vulnerability to warming.

Smoke from extreme wildfires in Canada adversely affected air quality in many regions in 2023^1,2.

Increased frequency, severity, and duration of droughts and increased wildfire severity are impacting many conifer forests globally. Reforestation in these changing disturbance regimes requires tree seedlings capable of establishing in hotter and drier climates.

Cloud-to-ground (CG) lightning is a major source of summer wildfire ignition in the western United States (WUS). However, future projections of lightning are uncertain since lightning is not directly simulated by most global climate models. To address this issue, we use convolutional neural network (CNN)-based parameterizations of daily June-September CG lightning.

Despite widespread concern over increases in wildfire severity, the mechanisms underlying this trend remain unclear, hampering our ability to mitigate the severity of future fires.