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<front>
<journal-meta>
<journal-id journal-id-type="publisher">HESS</journal-id>
<journal-title-group>
<journal-title>Hydrology and Earth System Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">HESS</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Hydrol. Earth Syst. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1607-7938</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/hess-16-3383-2012</article-id>
<title-group>
<article-title>Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations &amp;ndash; a comparison of methods</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Gudmundsson</surname>
<given-names>L.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Bremnes</surname>
<given-names>J. B.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Haugen</surname>
<given-names>J. E.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Engen-Skaugen</surname>
<given-names>T.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>The Norwegian Meteorological Institute, Oslo, Norway</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>now at: Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland</addr-line>
</aff>
<pub-date pub-type="epub">
<day>21</day>
<month>09</month>
<year>2012</year>
</pub-date>
<volume>16</volume>
<issue>9</issue>
<fpage>3383</fpage>
<lpage>3390</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2012 L. Gudmundsson et al.</copyright-statement>
<copyright-year>2012</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/3.0/">https://creativecommons.org/licenses/by/3.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://hess.copernicus.org/articles/16/3383/2012/hess-16-3383-2012.html">This article is available from https://hess.copernicus.org/articles/16/3383/2012/hess-16-3383-2012.html</self-uri>
<self-uri xlink:href="https://hess.copernicus.org/articles/16/3383/2012/hess-16-3383-2012.pdf">The full text article is available as a PDF file from https://hess.copernicus.org/articles/16/3383/2012/hess-16-3383-2012.pdf</self-uri>
<abstract>
<p>The impact of climate change on water resources is usually assessed at
the local scale. However, regional climate models (RCMs) are known to
exhibit systematic biases in precipitation. Hence, RCM simulations
need to be post-processed in order to produce reliable estimates of
local scale climate. Popular post-processing approaches are based on
statistical transformations, which attempt to adjust the
distribution of modelled data such that it closely resembles the
observed climatology. However, the diversity of suggested methods
renders the selection of optimal techniques difficult and therefore
there is a need for clarification.  In this paper, statistical
transformations for post-processing RCM output are reviewed and
classified into (1) distribution derived transformations, (2) parametric
transformations and (3) nonparametric transformations, each differing
with respect to their underlying assumptions. A real world
application, using observations of 82 precipitation stations in
Norway, showed that nonparametric transformations have the highest
skill in systematically reducing biases in RCM precipitation.</p>
</abstract>
<counts><page-count count="8"/></counts>
</article-meta>
</front>
<body/>
<back>
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