摘要：Climate change will continue to bring about unprecedented climate extremes in the future,and buildings and infrastructure will be exposed to such conditions. To ensure that new and existingbuildings deliver satisfactory performance over their design lives, their performance under currentand future projected climates needs to be assessed by undertaking building simulations. Thisstudy prepares climate data needed for building simulations for 564 locations by bias-correctingthe Canadian Regional Climate Model version 4 (CanRCM4) large ensemble (LE) simulations withreference to observations. Technical validation results show that bias-correction effectively reducesthe bias associated with CanRCM4-LE simulations in terms of their marginal distributions and theinter-relationship between climate variables. To ensure that the range of projected climate changeimpacts are encompassed within these data sets, and to furthermore provide building moisture andenergy reference years, the reference year files were prepared from bias-corrected CanRCM4-LEsimulations and are comprised of a typical meteorological year for building energy applications, atypical and extreme moisture reference year, a typical downscaled year, an extreme warm year, andan extreme cold year.
