@article{fdi:010066201,
  title = {{I}ntercomparison of statistical and dynamical downscaling models under the {EURO}- and {MED}-{CORDEX} initiative framework : present climate evaluations},
  author = {{A}yar, {P}. {V}. and {V}rac, {M}. and {B}astin, {S}. and {C}arreau, {J}ulie and {D}eque, {M}. and {G}allardo, {C}.},
  editor = {},
  language = {{ENG}},
  abstract = {{G}iven the coarse spatial resolution of {G}eneral {C}irculation {M}odels, finer scale projections of variables affected by local-scale processes such as precipitation are often needed to drive impacts models, for example in hydrology or ecology among other fields. {T}his need for high-resolution data leads to apply projection techniques called downscaling. {D}ownscaling can be performed according to two approaches: dynamical and statistical models. {T}he latter approach is constituted by various statistical families conceptually different. {I}f several studies have made some intercomparisons of existing downscaling models, none of them included all those families and approaches in a manner that all the models are equally considered. {T}o this end, the present study conducts an intercomparison exercise under the {EURO}- and {MED}-{CORDEX} initiative hindcast framework. {S}ix {S}tatistical {D}ownscaling {M}odels ({SDM}s) and five {R}egional {C}limate {M}odels ({RCM}s) are compared in terms of precipitation outputs. {T}he downscaled simulations are driven by the {ERA}interim reanalyses over the 1989-2008 period over a common area at 0.44{A} degrees of resolution. {T}he 11 models are evaluated according to four aspects of the precipitation: occurrence, intensity, as well as spatial and temporal properties. {F}or each aspect, one or several indicators are computed to discriminate the models. {T}he results indicate that marginal properties of rain occurrence and intensity are better modelled by stochastic and resampling-based {SDM}s, while spatial and temporal variability are better modelled by {RCM}s and resampling-based {SDM}. {T}hese general conclusions have to be considered with caution because they rely on the chosen indicators and could change when considering other specific criteria. {T}he indicators suit specific purpose and therefore the model evaluation results depend on the end-users point of view and how they intend to use with model outputs. {N}evertheless, building on previous intercomparison exercises, this study provides a consistent intercomparison framework, including both {SDM}s and {RCM}s, which is designed to be flexible, i.e., other models and indicators can easily be added. {M}ore generally, this framework provides a tool to select the downscaling model to be used according to the statistical properties of the local-scale climate data to drive properly specific impact models.},
  keywords = {{ZONE} {MEDITERRANEENNE} ; {S}tatistical downscaling ; {D}ynamical downscaling ; {CORDEX} ; {P}recipitation ; {I}ntercomparison ; {EUROPE} ; {MEDITERRANEE}},
  booktitle = {},
  journal = {{C}limate {D}ynamics},
  volume = {46},
  numero = {3-4},
  pages = {1301--1329},
  ISSN = {0930-7575},
  year = {2016},
  DOI = {10.1007/s00382-015-2647-5},
  URL = {https://www.documentation.ird.fr/hor/fdi:010066201},
}