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    <responseDate>2026-10-13T14:05:41Z</responseDate>
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    <identifier>10.57760/sciencedb.39249</identifier>
    <datestamp>2026-06-16T11:45:52Z</datestamp>
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  <dc:date>2026-06-16</dc:date>
  <dc:title>Homogenized Monthly and Daily Relative Humidity Observations in China</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.39249</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>The observational network for relative humidity in China, including more than 2400 meteorological stations, experienced nearly simultaneously automation in the 2000s, followed by frequent replacements of automated sensors in the 2010s. The introduced data inhomogeneities cannot be easily addressed by the conventional neighbor-comparison homogenization schemes, and are very likely to bias the estimate for long-term changes of this important hydrometeorological variable and associated extremes. By introducing well-documented metadata into a stepwise statistical test, we systematically detected breakpoints in the monthly and daily observational humidity records over 1951-2021, to which the Quantile Matching correction was applied. The homogenization proves to be effective to adjust for non-climatic discontinuities in 1936 stations, sourced from instrumentation changes, station relocations, and changing observation practices respectively. Based on the homogenized dataset, relative humidity over China shows an insignificantly weak drying trend during 1961&amp;ndash;2021, as opposed to the strong and significant decline suggested by raw observations.This homogenized dataset holds promises in alleviating the long-standing observation-model discrepancy in RH change, and thereby improves the attribution and projection capabilities to better understand its past and future changes as well as implications for diverse sectors.&amp;nbsp;The dataset contains homogenized monthly and daily relative humidity data and accompanying station information from 373 international exchange stations.</dc:description>
  <dc:subject>Relative humidity; Homogenization; Station metadata; Reference series; China</dc:subject>
  <dc:creator>Zhu Yani</dc:creator>
  <dc:creator>Cao Lijuan</dc:creator>
  <dc:creator>Zhou Zijiang</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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