Journal of Climate · 2021 · 14 citations · 55 references
Natural SciencesEarth SciencesHydrologyClimate ScienceClimatologyHydrometeorologyHydrometeorological Data AnalysisPrecipitationGauge-based Precipitation AnalysisGauge-corrected Precipitation EstimatesPrecipitation Gauge MeasurementsPrecipitation Intensity AnalysisPrecipitation ObservationsRainfall AmountReanalysis ProductsReanalysis Studies
Abstract Precipitation is spatially and temporally unevenly distributed. The unevenness of precipitation is crucial for climate change, as well as for water resource management, environmental risk reduction and industrial/agricultural production. In this study, gauge observations and eight reanalysis products are used to examine the unevenness of precipitation from 1979 to 2018 over China. The results show that all the reanalysis datasets can reproduce the spatial pattern of the annual number of wet days and precipitation intensity, as shown in the observations; however, most reanalyses overestimate the former and underestimate the latter. The mean cumulative fractions of the precipitation amount on the wettest 1, 5, and 10 days to annual total are approximately 9.3%, 29.8% and 45.1% in the gauge observations, and are 6.6%±0.8%, 22.1%±2.5% and 34.3%±3.5% in the reanalyses. The mean cumulative fractions of precipitation amount on the wettest 1, 5, and 10 days to annual total displays a small negative trend based on gauge observations over China (-0.06%/decade, -0.10%/decade and -0.10%/decade, respectively), but are positive and stronger in the eight current reanalyses (0.08% ± 0.08%/decade, 0.25%±0.08%/decade and 0.35%±0.10%/decade). The Japanese 55-year reanalysis is the best in quantifying the annual variability of the cumulative fractions of precipitation on the wettest 1, 5, and 10 days over China, while the ERA-Interim is the best in reflecting their trends. The reanalyses agree best with the observations in reflecting cumulative fractions of precipitation in the Yangtze River Basin and the worst in the Northwest China.
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