A statistical approach to multi-site multivariate downscaling of daily extreme temperature series



Downscaling methods for describing the linkage between global-scale climate variables and local climatic conditions have been frequently used in climate-related impact assessment studies. Previous works, however, have been mainly dealing with downscaling of climatic processes for a single site, but very few studies are concerned with the downscaling of these processes for multi-sites because of the complexity in accurately describing both observed at-site temporal persistence and spatial dependence between different locations. In the present study, a multi-site multivariate statistical downscaling (SD) approach was developed for simulating daily maximum (Tmax) and minimum (Tmin) temperature series at many sites concurrently. The proposed approach consists of a combination of a linear regression component to describe the linkage between global climate predictors and local temperature extremes, and a stochastic component based on a spatial moving average process to reproduce the observed spatial dependence between temperature extremes at different sites. The feasibility of the suggested SD method was assessed using observed daily extreme temperature data available at 10 weather stations located in the southwest region of Quebec and the southeast region of Ontario in Canada, as well as climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset for the 1961–1990 period. It was found that the proposed SD approach was able to accurately describe various Tmax and Tmin characteristics, including their spatial and temporal variation as well as their interannual anomalies. In addition, comparison of the results from the proposed multi-site multivariate SD method and one simulation series from the Canadian Regional Climate Model (CRCM) at a 45-km resolution (a dynamic downscaling procedure) has indicated that the suggested SD approach was able to more accurately describe the observed spatial and temporal characteristics of extreme temperature series at the regional scale than the CRCM-based dynamic downscaling method. Copyright © 2011 Royal Meteorological Society