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California has experienced enhanced extreme wildfire behaviour in recent years13, leading to substantial loss of life and property45. Some portion ofthe change in wildfire behaviour is attributable to anthropogenic climate warming, but formally quantifying this contribution is difficult because of numerous confounding factors· · 6 7 and because wildfires are below the grid scale of global climate models. Here we use machine learningto quantify empirical relationships between temperature (as well as the influence oftemperature on aridity) and the risk of extreme daily wildfire growth (>10,000 acres) in California and find thatthe influence oftemperature on the riskis primarily mediated through its influence on fuel moisture. We use the uncovered relationships to estimate the changes in extreme daily wildfire growth risk under anthropogenic warming by subjecting historical fires from 2003 to 2020 to differing background climatological temperatures and aridity conditions. We find that the influence of anthropogenic warming on the risk of extreme daily wildfire growth varies appreciably on a fire-by-fire and day-by-day basis, depending on whether or not climate warming pushes conditions over certain thresholds of aridity, such as 1.5 kPa ofvapour-pressure deficit and 10% dead fuel moisture. So far, anthropogenic warming has enhanced the aggregate expected frequency of extreme daily wildfire growth by 25% (5-95 range of 14-36%), on average, relative to preindustrial conditions. But for some fires, there was approximately no change, and for other fires, the enhancement has been as much as 461%. When historical fires are subjected to a range of projected end-of-century conditions, the aggregate expected frequency of extreme daily wildfire growth events increases by 59% (5-95 range of 47-71%) under a low SSP1-2.6 emissions scenario compared with an increase of 172% (5-95 range of156-188%) under a very high SSP5-8.5 emissions scenario, relative to preindustrial conditions.
Physics-based models are typically the preferred means of quantifying the contribution of increased greenhouse gas concentrations to weather and climate extremes8. However, high-resolution physics-based models capable of simulating fire behaviour at daily timescales and kilometre spatial scales9 are too computationally expensive to easily incorporate into climate change studies. Many dynamic global vegetation models designed for climate change studies simulate fire characteristics10 but they output at spatiotemporal resolutions too coarse to make inferences about the extreme daily growth ofindividual fires (Supplementary Information 22). These practical...