@article{fdi:010055795,
  title = {{S}tatistical downscaling of the {F}rench {M}editerranean climate : assessment for present and projection in an anthropogenic scenario},
  author = {{L}avaysse, {C}. and {V}rac, {M}. and {D}robinski, {P}. and {L}engaigne, {M}atthieu and {V}ischel, {T}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he {M}editerranean basin is a particularly vulnerable region to climate change, featuring a sharply contrasted climate between the {N}orth and {S}outh and governed by a semi-enclosed sea with pronounced surrounding topography covering parts of the {E}urope, {A}frica and {A}sia regions. {T}he physiographic specificities contribute to produce mesoscale atmospheric features that can evolve to high-impact weather systems such as heavy precipitation, wind storms, heat waves and droughts. {T}he evolution of these meteorological extremes in the context of global warming is still an open question, partly because of the large uncertainty associated with existing estimates produced by global climate models ({GCM}) with coarse horizontal resolution (similar to 200 km). {D}ownscaling climatic information at a local scale is, thus, needed to improve the climate extreme prediction and to provide relevant information for vulnerability and adaptation studies. {I}n this study, we investigate wind, temperature and precipitation distributions for past recent climate and future scenarios at eight meteorological stations in the {F}rench {M}editerranean region using one statistical downscaling model, referred as the '{C}umulative {D}istribution {F}unction transform' ({CDF}-t) approach. {A} thorough analysis of the uncertainty associated with statistical downscaling and bi-linear interpolation of large-scale wind speed, temperature and rainfall from reanalyses ({ERA}-40) and three {GCM} historical simulations, has been conducted and quantified in terms of {K}olmogorov-{S}mirnov scores. {CDF}-t produces a more accurate and reliable local wind speed, temperature and rainfall. {G}enerally, wind speed, temperature and rainfall {CDF} obtained with {CDF}-t are significantly similar with the observed {CDF}, even though {CDF}-t performance may vary from one station to another due to the sensitivity of the driving large-scale fields or local impact. {CDF}-t has then been applied to climate simulations of the 21st century under {B}1 and {A}2 scenarios for the three {GCM}s. {A}s expected, the most striking trend is obtained for temperature (median and extremes), whereas for wind speed and rainfall, the evolution of the distributions is weaker. {M}ean surface wind speed and wind extremes seem to decrease in most locations, whereas the mean rainfall value decreases while the extremes seem to slightly increase. {T}his is consistent with previous studies, but if this trend is clear with wind speed and rainfall data interpolated from {GCM} simulations at station locations, conversely {CDF}-t produces a more uncertain trend.},
  keywords = {},
  booktitle = {},
  journal = {{N}atural {H}azards and {E}arth {S}ystem {S}ciences},
  volume = {12},
  numero = {3},
  pages = {651--670},
  ISSN = {1561-8633},
  year = {2012},
  DOI = {10.5194/nhess-12-651-2012},
  URL = {https://www.documentation.ird.fr/hor/fdi:010055795},
}