Climatic Change

, Volume 127, Issue 3–4, pp 547–560 | Cite as

Statistical downscaling of climate impact indices: testing the direct approach

  • A. CasanuevaEmail author
  • M. D. Frías
  • S. Herrera
  • D. San-Martín
  • K. Zaninovic
  • J. M. Gutiérrez


Climate Impact Indices (CIIs) are being increasingly used in different socioeconomic sectors to transfer information about climate change impacts to stakeholders. Typically, CIIs comprise into a single index several weather variables —such as temperature, wind speed, precipitation and humidity— which are relevant for a particular problem of interest. Moreover, most of the CIIs require daily (or monthly) physical coherence among these variables for their proper calculation. This constraints the number of statistical downscaling techniques suitable for a component-wise approach to this problem. We test the suitability of the alternative “direct” downscaling approach in which the downscaling method is applied directly to the CII, thus circumventing the multi-variable problem and allowing the use of a wider range of downscaling methods. For illustrative purposes, we consider two popular CIIs —the Fire Weather Index (FWI) and the Physiological Equivalent Temperature (PET), used in the wildfire and tourism sectors, respectively— and compare the performance of the two approaches using the analog method, a simple and popular method providing inter-variable dependence. The results obtained with ‘perfect’ reanalysis predictors are comparable for both approaches, although smaller accuracy is obtained in general with the direct approach. Moreover, similar climate change ‘deltas’ are obtained with both approaches when applied to an illustrative future global projection using the ECHAM5 model. Overall, there is a trade-off between performance and simplicity which needs to be balanced for each particular application.


Statistical Downscaling Physiological Equivalent Temperature Analog Method Fire Danger Local Standard Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Authors are grateful to the data providers and also to Dr. Matzarakis for providing the RayMan software and to J. Bedia por their helpful comments. We also acknowledge the financial support from the European Commision’s Seventh Framework Programme under CLIM-RUN Project (contract FP7-ENV-2010-265192). A.C. thanks to the Spanish Ministry of Science and Innovation for the funding provided within the FPI programme (CORWES project, CGL2010-22158-C02: BES-2011-047612) and J.M.G. for the grant EXTREMBLES (CGL2010-21869). We thank three anonymous referees for their useful comments that helped to improve the original manuscript.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • A. Casanueva
    • 1
    Email author
  • M. D. Frías
    • 1
  • S. Herrera
    • 1
    • 2
  • D. San-Martín
    • 2
  • K. Zaninovic
    • 3
  • J. M. Gutiérrez
    • 4
  1. 1.Grupo de Meteorología, Dpto. Matemática Aplicada y Ciencias de la ComputaciónUniv. de CantabriaSantanderSpain
  2. 2.Predictia Intelligent Data Solutions S.L.SantanderSpain
  3. 3.Meteorological and Hydrological Service of CroatiaZagrebCroatia
  4. 4.Grupo de Meteorología, Instituto de Física de CantabriaCSIC-Univ. de CantabriaSantanderSpain

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