A comparison of statistical downscaling methods suited for wildfire applications

Abstract Place‐based data is required in wildfire analyses, particularly in regions of diverse terrain that foster not only strong gradients in meteorological variables, but also complex fire behaviour. However, a majority of downscaling methods are inappropriate for wildfire application due to the lack of daily timescales and variables such as humidity and winds that are important for fuel flammability and fire spread. Two statistical downscaling methods, the daily Bias corrected Spatial Downscaling (BCSD) and the Multivariate Adapted Constructed Analogs (MACA) that directly incorporate daily data from global climate models, were validated over the western US using global reanalysis data. While both methods…

• NS
  National Science Foundation
• JF
  Joint Fire Science Program
• UF
  U.S. Forest Service