Recent years have seen unprecedented fire activity at high latitudes and knowledge of future wildfire risk is key for adaptation and risk management. Here we present a systematic characterization of the probability distributions (PDFs) of fire weather conditions, and how it arises from underlying meteorological drivers of change, in five boreal forest regions, for pre-industrial conditions and different global warming levels. Using initial condition ensembles from two global climate models to characterize regional variability, we quantify the PDFs of daily maximum surface air temperature (SATmax), precipitation, wind, and minimum relative humidity (RHmin), and their evolution with global temperature. The resulting aggregate change in fire risk is quantified using the Canadian Fire Weather Index (FWI). 
In all regions we find increases in both means and upper tails of the FWI distribution, and a widening suggesting increased variability. The main underlying drivers are the projected increase in mean daily SATmax and decline in RHmin, marked already at +1 and +2°C global warming. The largest changes occur in Canada, where we estimate a doubling of days with moderate-or-higher FWI between +1°C and +4°C global warming, and the smallest in Alaska. While both models exhibit the same general features of change with warming, differences in magnitude of the shifts exist, particularly for RHmin, where the bias compared to reanalysis is also largest. Given its importance for the FWI, RHmin evolution is identified as an area in need of further research. 
While occurrence and severity of wildfires ultimately depend also on factors such as ignition and fuel, we show how improved knowledge of meteorological conditions conducive to high wildfire risk, already changing across the high latitudes, can be used as a first indication of near-term changes. Our results confirm that continued global warming can rapidly push boreal forest regions into increasingly unfamiliar fire weather regimes.

<p>Recent years have seen unprecedented fire activity at Arctic latitudes, leading to severe consequences including unhealthy air quality in high latitude towns and cities. While wildfire occurrence and severity result from a complex interplay between natural and anthropogenic factors, weather is a key factor.</p><p>Weather conditions that promote high wildfire risk are characterized by the combination of high temperatures, little precipitation and low humidity, and often high winds. All of these can be affected by human-induced climate change and evidence is emerging that wildfire risk is already increasing in many regions. Such changes not only manifest as shifts in the means and extremes of the weather variables but can also be changes in the shape of their distributions. The importance of the full, regional Probability Density Functions (PDFs) of individual and aggregated variables, which contain information on expected weather not apparent from the distribution mean or tails, but through changes to their overall shape, for understanding climate risk has been broadly discussed in the literature. Furthermore, while simulations with regional climate models to derive such information are costly and time consuming, the advent of large ensembles of coupled climate model simulations has recently opened new opportunities.</p><p>Here we present a detailed characterization of the distribution and variability of weather variables conducive to wildfire risk across five high-latitude boreal regions in North America, Scandinavia and Russia. Building on methodology developed in Samset et al. (2019), we quantify the PDFs of daily data for a broad set of individual variables, as well as for the aggregate change expressed using the Canadian Fire Weather Index. Using ensembles of coupled simulations from two climate models (CanESM5 and MPI-ESM1-2) and two CMIP6 scenarios (the Shared Socioeconomic Pathways SSP1-2.6 and SSP5-8.5), we consistently quantify the changes of regionally and seasonally resolved PDFs under different levels of global warming.  </p><p>Our results provide a comprehensive picture of the potential future changes in drivers of fire weather and wildfire risk in the pan-Arctic region and demonstrate the difference between regions. We also show how statistical descriptions combined with emulation of Earth System Model (ESM) information can offer an alternative pathway to resource demanding model runs, for rapidly translating science to user-oriented information.</p>