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As the risk of a forest fire is largely influenced by weather, evaluatingits tendency under a changing climate becomes important for management anddecision making. Currently, biases in climate models make it difficult torealistically estimate the future climate and consequent impact on firerisk. A distribution-based scaling (DBS) approach was developed as apost-processing tool that intends to correct systematic biases in climatemodelling outputs. In this study, we used two projections, one driven byhistorical reanalysis (ERA40) and one from a global climate model (ECHAM5)for future projection, both having been dynamically downscaled by a regionalclimate model (RCA3). The effects of the post-processing tool on relativehumidity and wind speed were studied in addition to the primary variablesprecipitation and temperature. Finally, the Canadian Fire Weather Indexsystem was used to evaluate the influence of changing meteorologicalconditions on the moisture content in fuel layers and the fire-spread risk.The forest fire risk results using DBS are proven to better reflect riskusing observations than that using raw climate outputs. For future periods,southern Sweden is likely to have a higher fire risk than today, whereasnorthern Sweden will have a lower risk of forest fire

Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden