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Characterizing the physical and dynamic meteorology of wildland fires has obvious socioeconomic importance and is necessary to develop not only firefighting but also mitigation strategies such as prescribed burns and effective fuel management practices such as forest thinning. However, despite significant progress over a century, there are shortcomings in our understanding of the physical processes governing wildland fire behavior. Although some research progress has been made in understanding how fires spread on grasslands, several aspects of fire behavior within the forest canopy environment are still not well-understood. This review is an attempt to organize the fluid mechanics of the mass, momentum, and energy transfer during wildland fire events through the lens of vegetation canopy turbulence. The structure, organization, and progress of the flame front and the buoyant plume through the canopy are shown to be intricately related to the coherent structures associated with fire–vegetation–atmosphere interaction, and potential future research directions are identified. 

Climate change is intensifying fire behavior, with the largest and fastest-spreading fires causing the greatest impacts on people and ecosystems. Yet the mechanisms driving variability and trends in large fires remain poorly understood. Using 12-hour satellite-derived fire tracking data from 2012 to 2023, we show that the merging of separate ignitions into multi-ignition complexes is a key process amplifying fire size and destructive potential across temperate and boreal ecoregions. Multi-ignition fires account for 31% of the burned area in California and 59% in the Arctic-boreal domain, spread faster and persist longer than single-ignition fires, and disproportionately contribute to extreme fire years in California, Canada, and Siberia. They also generate stronger atmospheric feedbacks, produce more pyrocumulonimbus events, and strain firefighting capacity by dispersing suppression resources. Recognizing and accounting for fire-merging dynamics are critical for improving wildfire prediction, risk assessment, and management.