Climate Dynamics

, Volume 43, Issue 12, pp 3201–3217 | Cite as

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada

  • Carlos F. GaitanEmail author
  • William W. Hsieh
  • Alex J. Cannon


Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.


Statistical downscaling Nonlinear methods Climate extremes Precipitation Future evaluation Artificial neural networks 



The authors would like to acknowledge the data access integration (DAI) Team for providing the data (CGCM3 and CRCM 4.2) and technical support. The DAI Portal ( is made possible through collaboration among the Global Environmental and Climate Change Centre (GEC3), the Adaptation and Impacts Research Division (AIRD) of Environment Canada, and the drought research initiative (DRI). The Ouranos Consortium (in Quebec) provides access of the CRCM. We are grateful for the support from a Special Research Opportunity grant awarded by the Canadian Natural Sciences and Engineering Research Council, and to Prof. Van Nguyen for leading the multi-university project. This work is part of the “Probabilistic assessment of regional changes in climate variability and extremes” project funded by the Natural Sciences and Engineering Research Council of Canada through a Special Research Opportunity (NSERC-SRO) grant. The project aims to develop high-resolution climate change information with the Canadian GCMs and different downscaling methodologies.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Carlos F. Gaitan
    • 1
    • 3
    Email author
  • William W. Hsieh
    • 1
  • Alex J. Cannon
    • 1
    • 2
  1. 1.Department of Earth, Ocean and Atmospheric Sciences, 2020-2207 Main MallUniversity of British ColumbiaVancouverCanada
  2. 2.Pacific Climate Impacts ConsortiumUniversity of VictoriaVictoriaCanada
  3. 3.South Central Climate Science CenterNOAA-GFDLPrincetonUSA

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