• Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419, 224232, https://doi.org/10.1038/nature01092.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barbero, R., H. J. Fowler, G. Lenderink, and S. Blenkinsop, 2017: Is the intensification of precipitation extremes with global warming better detected at hourly than daily resolutions? Geophys. Res. Lett., 44, 974983, https://doi.org/10.1002/2016GL071917.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beek, C. Z., H. Leijnse, P. J. J. F. Torfs, and R. Uijlenhoet, 2012: Seasonal semi-variance of Dutch rainfall at hourly to daily scales. Adv. Water Resour., 45, 7685, https://doi.org/10.1016/j.advwatres.2012.03.023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellprat, O., V. Guemas, F. Doblas-Reyes, and M. G. Donat, 2019: Towards reliable extreme weather and climate event attribution. Nat. Commun., 10, 1732, https://doi.org/10.1038/s41467-019-09729-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beranova, R., J. Kysely, and M. Hanel, 2018: Characteristics of sub-daily precipitation extremes in observed data and regional climate model simulations. Theor. Appl. Climatol., 132, 515527, https://doi.org/10.1007/s00704-017-2102-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, P., C. Moseley, and J. O. Haerter, 2013: Strong increase in convective precipitation in response to higher temperatures. Nat. Geosci., 6, 181185, https://doi.org/10.1038/ngeo1731.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boisvert, L. N., M. A. Webster, A. A. Petty, T. Markus, D. H. Bromwich, and R. I. Cullather, 2018: Intercomparison of precipitation estimates over the Arctic Ocean and its peripheral seas from reanalyses. J. Climate, 31, 84418462, https://doi.org/10.1175/JCLI-D-18-0125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., J. Y. Chen, F. R. Robertson, and R. F. Adler, 2008: Evaluation of global precipitation in reanalyses. J. Appl. Meteor. Climatol., 47, 22792299, https://doi.org/10.1175/2008JAMC1921.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, V. M., B. D. Keim, and A. W. Black, 2019: Climatology and trends in hourly precipitation for the southeast United States. J. Hydrometeor., 20, 17371755, https://doi.org/10.1175/JHM-D-19-0004.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, V. M., B. D. Keim, and A. W. Black, 2020: Trend analysis of multiple extreme hourly precipitation time series in the southeastern United States. J. Appl. Meteor. Climatol., 59, 427442, https://doi.org/10.1175/JAMC-D-19-0119.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cattoen, C., D. E. Robertson, J. C. Bennett, Q. J. Wang, and T. K. Carey-Smith, 2020: Calibrating hourly precipitation forecasts with daily observations. J. Hydrometeor., 21, 16551673, https://doi.org/10.1175/JHM-D-19-0246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, M., B. Liu, C. Martinez-Villalobos, G. Ren, S. Li, and T. Zhou, 2020: Changes in extreme precipitation accumulations during the warm season over continental China. J. Climate, 33, 10 79910 811, https://doi.org/10.1175/JCLI-D-20-0616.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G. X., R. Y. Lan, W. X. Zeng, H. Pan, and W. B. Li, 2018: Diurnal variations of rainfall in surface and satellite observations at the monsoon coast (South China). J. Climate, 31, 17031724, https://doi.org/10.1175/JCLI-D-17-0373.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y., 2020: Increasingly uneven intra-seasonal distribution of daily and hourly precipitation over eastern China. Environ. Res. Lett., 15, 104068, https://doi.org/10.1088/1748-9326/abb1f1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., M. Balmaseda, G. Balsamo, R. Engelen, and J. N. Thépaut, 2014: Toward a consistent reanalysis of the climate system. Bull. Amer. Meteor. Soc., 95, 12351248, https://doi.org/10.1175/BAMS-D-13-00043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunkerley, D., 2015: Intra-event intermittency of rainfall: An analysis of the metrics of rain and no-rain periods. Hydrol. Processes, 29, 32943305, https://doi.org/10.1002/hyp.10454.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fekete, B. M., C. J. Vörösmarty, J. O. Roads, and C. J. Willmott, 2004: Uncertainties in precipitation and their impacts on runoff estimates. J. Climate, 17, 294304, https://doi.org/10.1175/1520-0442(2004)017<0294:UIPATI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Freitas, E. S., and Coauthors, 2020: The performance of the IMERG satellite-based product in identifying sub-daily rainfall events and their properties. J. Hydrol., 589, 125128, https://doi.org/10.1016/j.jhydrol.2020.125128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gimeno, L., A. Drumond, R. Nieto, R. M. Trigo, and A. Stohl, 2010: On the origin of continental precipitation. Geophys. Res. Lett., 37, L13804, https://doi.org/10.1029/2010GL043712.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gimeno, L., R. Nieto, A. Drumond, R. Castillo, and R. Trigo, 2013: Influence of the intensification of the major oceanic moisture sources on continental precipitation. Geophys. Res. Lett., 40, 14431450, https://doi.org/10.1002/grl.50338.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goudenhoofdt, E., and L. Delobbe, 2009: Evaluation of radar-gauge merging methods for quantitative precipitation estimates. Hydrol. Earth Syst. Sci., 13, 195203, https://doi.org/10.5194/hess-13-195-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guerreiro, S. B., H. J. Fowler, R. Barbero, S. Westra, G. Lenderink, S. Blenkinsop, E. Lewis, and X. F. Li, 2018: Detection of continental-scale intensification of hourly rainfall extremes. Nat. Climate Change, 8, 803807, https://doi.org/10.1038/s41558-018-0245-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, Z., Q. Zhang, and J. Sun, 2016: The contribution of mesoscale convective systems to intense hourly precipitation events during the warm seasons over central East China. Adv. Atmos. Sci., 33, 12331239, https://doi.org/10.1007/s00376-016-6034-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, S., J. Yang, Q. Bao, L. Wang, and B. Wang, 2019: Fidelity of the observational/reanalysis datasets and global climate models in representation of extreme precipitation in East China. J. Climate, 32, 195212, https://doi.org/10.1175/JCLI-D-18-0104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hegerl, G. C., and Coauthors, 2014: Challenges in quantifying changes in the global water cycle. Bull. Amer. Meteor. Soc., 96, 10971115, https://doi.org/10.1175/BAMS-D-13-00212.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and D. Dee, 2018: ERA5 reanalysis is in production. ECMWF Newsletter, No. 147, ECMWF, Reading, United Kingdom, 7, https://www.ecmwf.int/en/elibrary/16299-newsletter-no-147-spring-2016.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

  • Hoffmann, L., and Coauthors, 2019: From ERA-Interim to ERA5: The considerable impact of ECMWF’s next-generation reanalysis on Lagrangian transport simulations. Atmos. Chem. Phys., 19, 30973124, https://doi.org/10.5194/acp-19-3097-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, X., and W. Yuan, 2021: Evaluation of ERA5 precipitation over the eastern periphery of the Tibetan Plateau from the perspective of regional rainfall events. Int. J. Climatol., 41, 26252637, https://doi.org/10.1002/joc.6980.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, X., T. Zhou, W. Zhang, J. Jiang, P. Li, and Y. Zhao, 2019: Northern hemisphere land monsoon precipitation changes in the twentieth century revealed by multiple reanalysis datasets. Climate Dyn., 53, 71317149, https://doi.org/10.1007/s00382-019-04982-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, Q., and Coauthors, 2021: Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland. J. Hydrol., 595, 125660, https://doi.org/10.1016/j.jhydrol.2020.125660.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiao, D., N. Xu, F. Yang, and K. Xu, 2021: Evaluation of spatial-temporal variation performance of ERA5 precipitation data in China. Sci. Rep., 11, 17956, https://doi.org/10.1038/s41598-021-97432-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., A. Becker, G. J. Huffman, C. L. Muller, P. Joe, G. Skofronick-Jackson, and D. B. Kirschbaum, 2017: So, how much of the Earth’s surface is covered by rain gauges? Bull. Amer. Meteor. Soc., 98, 6978, https://doi.org/10.1175/BAMS-D-14-00283.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kursinski, A. L., and X. Zeng, 2006: Areal estimation of intensity and frequency of summertime precipitation over a midlatitude region. Geophys. Res. Lett., 33, L22401, https://doi.org/10.1029/2006GL027393.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lehner, F., A. W. Wood, J. A. Vano, D. M. Lawrence, M. P. Clark, and J. S. Mankin, 2019: The potential to reduce uncertainty in regional runoff projections from climate models. Nat. Climate Change, 9, 926933, https://doi.org/10.1038/s41558-019-0639-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, C., T. Zhao, C. Shi, and Z. Liu, 2020: Evaluation of daily precipitation product in China from the CMA global atmospheric interim reanalysis. J. Meteor. Res., 34, 117136, https://doi.org/10.1007/s13351-020-8196-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, R., K. Wang, and D. Qi, 2021: Event-based evaluation of the GPM multisatellite merged precipitation product from 2014 to 2018 over China: Methods and results. J. Geophys. Res. Atmos., 126, e2020JD033692, https://doi.org/10.1029/2020JD033692.

    • Search Google Scholar
    • Export Citation
  • Li, Y., B. Guo, K. Wang, G. Wu, and C. Shi, 2020: Performance of TRMM product in quantifying frequency and intensity of precipitation during Daytime and nighttime across China. Remote Sens., 12, 740, https://doi.org/10.3390/rs12040740.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, M., and P. Huybers, 2019: If rain falls in India and no one reports it, are historical trends in monsoon extremes biased? Geophys. Res. Lett., 46, 16811689, https://doi.org/10.1029/2018GL079709.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, R., T. Zhou, and Y. Qian, 2014: Evaluation of global monsoon precipitation changes based on five reanalysis datasets. J. Climate, 27, 12711289, https://doi.org/10.1175/JCLI-D-13-00215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., and Z. Ren, 2005: Progress in quality control of surface meteorological data (in Chinese). Meteor. Sci. Technol., 33, 199203.

    • Search Google Scholar
    • Export Citation
  • Lochbihler, K., G. Lenderink, and A. P. Siebesma, 2017: The spatial extent of rainfall events and its relation to precipitation scaling. Geophys. Res. Lett., 44, 86298636, https://doi.org/10.1002/2017GL074857.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loriaux, J., G. Lenderink, and A. P. Siebesma, 2016: Peak precipitation intensity in relation to atmospheric conditions and large-scale forcing at midlatitudes. J. Geophys. Res. Atmos., 121, 54715487, https://doi.org/10.1002/2015JD024274.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, Y., M. Wu, F. Ren, and J. Li, 2016: Synoptic situations of extreme hourly precipitation over China. J. Climate, 29, 87038719, https://doi.org/10.1175/JCLI-D-16-0057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marelle, L., G. Myhre, O. Hodnebrog, J. Sillmann, and B. H. Samset, 2018: The Changing seasonality of extreme daily precipitation. Geophys. Res. Lett., 45, 11 35211 360, https://doi.org/10.1029/2018GL079567.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marvel, K., M. Biasutti, C. Bonfils, K. E. Taylor, Y. Kushnir, and B. I. Cook, 2017: Observed and projected changes to the precipitation annual cycle. J. Climate, 30, 49834995, https://doi.org/10.1175/JCLI-D-16-0572.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Medina-Cobo, M. T., A. P. Garcia-Marin, J. Estevez, and J. L. Ayuso-Munoz, 2016: The identification of an appropriate Minimum Inter-event Time (MIT) based on multifractal characterization of rainfall data series. Hydrol. Processes, 30, 35073517, https://doi.org/10.1002/hyp.10875.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merino, A., E. Garcia-Ortega, A. Navarro, S. Fernandez-Gonzalez, F. J. Tapiador, and J. L. Sanchez, 2021: Evaluation of gridded rain-gauge-based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate. Int. J. Climatol., 41, 30273043, https://doi.org/10.1002/joc.7003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muschinski, T., and J. I. Katz, 2013: Trends in hourly rainfall statistics in the United States under a warming climate. Nat. Climate Change, 3, 577580, https://doi.org/10.1038/nclimate1828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nelson, B. R., O. P. Prat, D. J. Seo, and E. Habib, 2016: Assessment and implications of NCEP Stage IV quantitative precipitation estimates for product intercomparisons. Wea. Forecasting, 31, 371394, https://doi.org/10.1175/WAF-D-14-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newman, A. J., M. P. Clark, R. J. Longman, and T. W. Giambelluca, 2019: Methodological intercomparisons of station-based gridded meteorological products: Utility, limitations, and paths forward. J. Hydrometeor., 20, 531547, https://doi.org/10.1175/JHM-D-18-0114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nie, J., A. H. Sobel, D. A. Shaevitz, and S. Wang, 2018: Dynamic amplification of extreme precipitation sensitivity. Proc. Natl. Acad. Sci. USA, 115, 94679472, https://doi.org/10.1073/pnas.1800357115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., and R. Knutti, 2018: The uneven nature of daily precipitation and its change. Geophys. Res. Lett., 45, 11 98011 988, https://doi.org/10.1029/2018GL080298.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., R. M. Rasmussen, K. Ikeda, C. Liu, and M. P. Clark, 2017: The future intensification of hourly precipitation extremes. Nat. Climate Change, 7, 4853, https://doi.org/10.1038/nclimate3168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qin, S., K. Wang, G. Wu, and Z. Ma, 2021: Variability of hourly precipitation during the warm season over eastern China using gauge observations and ERA5. Atmos. Res., 264, 105872, https://doi.org/10.1016/j.atmosres.2021.105872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajagopalan, B., U. Lall, and D. G. Tarboton, 1997: Evaluation of kernel density estimation methods for daily precipitation resampling. Stoch. Hydrol. Hydraul., 11, 523547, https://doi.org/10.1007/BF02428432.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasanen, M., M. Chung, M. Katurji, P. Pellikka, J. Rinne, and G. G. Katul, 2018: Similarity in fog and rainfall intermittency. Geophys. Res. Lett., 45, 10 69110 699, https://doi.org/10.1029/2018GL078837.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rivoire, P., O. Martius, and P. Naveau, 2021: A comparison of moderate and extreme ERA-5 daily precipitation with two observational data sets. Earth Space Sci., 8, e2020EA001633, https://doi.org/10.1029/2020EA001633.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Q. H., C. Y. Miao, Q. Y. Duan, H. Ashouri, S. Sorooshian, and K. L. Hsu, 2018: A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Rev. Geophys., 56, 79107, https://doi.org/10.1002/2017RG000574.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, Y., Q. H. Tang, Z. G. Wang, F. H. S. Chiew, X. J. Zhang, and H. Xiao, 2019: Different precipitation elasticity of runoff for precipitation increase and decrease at watershed scale. J. Geophys. Res. Atmos., 124, 11 93211 943, https://doi.org/10.1029/2018JD030129.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tapiador, F. J., R. Roca, A. Del Genio, B. Dewitte, W. Petersen, and F. Zhang, 2019: Is precipitation a good metric for model performance? Bull. Amer. Meteor. Soc., 100, 223234, https://doi.org/10.1175/BAMS-D-17-0218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and Y. Zhang, 2018: How often does it really rain. Bull. Amer. Meteor. Soc., 99, 289298, https://doi.org/10.1175/BAMS-D-17-0107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 12051217, https://doi.org/10.1175/BAMS-84-9-1205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., Y. Zhang, and M. Gehne, 2017: Intermittency in precipitation: Duration, frequency, intensity and amounts using hourly data. J. Hydrometeor., 18, 13931412, https://doi.org/10.1175/JHM-D-16-0263.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., J. Wang, Z. Ren, W. Li, Y. Lei, and M. Tu, 2009: Automatized observational experiment on solid precipitation (in Chinese) . Meteor. Sci. Technol., 37, 97101.

    • Search Google Scholar
    • Export Citation
  • White, R. H., D. S. Battisti, and G. Skok, 2017: Tracking precipitation events in time and space in gridded observational data. Geophys. Res. Lett., 44, 86378646, https://doi.org/10.1002/2017GL074011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wootten, A., and R. P. Boyles, 2014: Comparison of NCEP multisensor precipitation estimates with independent gauge data over the Eastern United States. J. Appl. Meteor. Climatol., 53, 28482862, https://doi.org/10.1175/JAMC-D-14-0034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, G., and K. Wang, 2021: Observed response of precipitation intensity to dew point temperature over the contiguous US. Theor. Appl. Climatol., 144, 13491362, https://doi.org/10.1007/s00704-021-03602-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ye, J. S., W. H. Li, L. F. Li, and F. Zhang, 2013: “North drying and south wetting” summer precipitation trend over China and its potential linkage with aerosol loading. Atmos. Res., 125, 1219, https://doi.org/10.1016/j.atmosres.2013.01.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yin, J. B., P. Gentine, S. Zhou, S. C. Sullivan, R. Wang, Y. Zhang, and S. L. Guo, 2018: Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat. Commun., 9, 4389, https://doi.org/10.1038/s41467-018-06765-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, R. C., Y. P. Xu, T. J. Zhou, and J. Li, 2007: Relation between rainfall duration and diurnal variation in the warm season precipitation over central eastern China. Geophys. Res. Lett., 34, L13703, https://doi.org/10.1029/2007GL030315.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Q., Y. Zhao, and S. Fan, 2016: Development of hourly precipitation datasets for national meteorological stations in China (in Chinese). Torrential Rain Disaster, 35, 182186.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., L. Alexander, G. C. Hegerl, P. Jones, A. K. Tank, T. C. Peterson, B. Trewin, and F. W. Zwiers, 2011: Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip. Rev.: Climate Change, 2, 851870, https://doi.org/10.1002/wcc.147.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., and K. Wang, 2021: Global precipitation system size. Environ. Res. Lett., 16, 054005, https://doi.org/10.1088/1748-9326/abf394.

  • Zhao, Y., X. Xu, T. Zhao, H. Xu, F. Mao, H. Sun, and Y. Wang, 2016: Extreme precipitation events in East China and associated moisture transport pathways. Science China, 59, 19841872, https://doi.org/10.1007/s11430-016-5315-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, Y. G., Y. D. Gong, J. Chen, and F. Y. Tian, 2019: Warm-season diurnal variations of total, stratiform, convective, and extreme hourly precipitation over central and eastern China. Adv. Atmos. Sci., 36, 143159, https://doi.org/10.1007/s00376-018-7307-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, C., and K. Wang, 2017: Contrasting daytime and nighttime precipitation variability between observations and eight reanalysis products from 1979 to 2014 in China. J. Climate, 30, 64436464, https://doi.org/10.1175/JCLI-D-16-0702.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Validation of Precipitation Events in ERA5 to Gauge Observations during Warm Seasons over Eastern China

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  • 1 aState Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
  • | 2 bMeteorological Center, Northwest Regional Air Traffic Management Bureau of Civil Aviation Administration of China, Xi’an, China
  • | 3 cNational Meteorological Center, China Meteorological Administration, Beijing, China
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Abstract

Precipitation events should be characterized using data with high temporal resolution, such as hourly precipitation. Event-based evaluation can provide more information than the traditional equal-time-interval method by considering precipitation intermittency. This study focuses on the performance of hourly gauge observations and ERA5 products based on precipitation events in eastern China during 1979–2015. The annual frequency, duration, amount, and intensity of precipitation events are compared, and the statistics of precipitation events with different durations are also evaluated. Results show that ERA5 estimated more annual precipitation events and longer duration compared to the gauge observations, with relative deviation values of 48.75% and 49.22% at the national scale. Precipitation intensity and amount estimated by ERA5 based on precipitation events were less than those obtained from gauge observations, and the discrepancies in low-latitude regions were greater than those in high-latitude areas. The frequency of precipitation events decreased exponentially with duration for both ERA5 and gauge observations, but generally the value for the former was larger than for the latter. The statistics related to precipitation events showed smaller trends for ERA5 than for gauge observations, i.e., −0.13 h decade−1 and −0.17 mm decade−1 for the trends of duration and amount in ERA5, which contrasts with 0.03 h decade−1 and 0.14 mm decade−1 for gauge observations, respectively. These results can provide a reference for improving the parameterization scheme of the precipitation triggering mechanism in the process of model simulation.

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Corresponding author: Guocan Wu, gcwu@bnu.edu.cn

Abstract

Precipitation events should be characterized using data with high temporal resolution, such as hourly precipitation. Event-based evaluation can provide more information than the traditional equal-time-interval method by considering precipitation intermittency. This study focuses on the performance of hourly gauge observations and ERA5 products based on precipitation events in eastern China during 1979–2015. The annual frequency, duration, amount, and intensity of precipitation events are compared, and the statistics of precipitation events with different durations are also evaluated. Results show that ERA5 estimated more annual precipitation events and longer duration compared to the gauge observations, with relative deviation values of 48.75% and 49.22% at the national scale. Precipitation intensity and amount estimated by ERA5 based on precipitation events were less than those obtained from gauge observations, and the discrepancies in low-latitude regions were greater than those in high-latitude areas. The frequency of precipitation events decreased exponentially with duration for both ERA5 and gauge observations, but generally the value for the former was larger than for the latter. The statistics related to precipitation events showed smaller trends for ERA5 than for gauge observations, i.e., −0.13 h decade−1 and −0.17 mm decade−1 for the trends of duration and amount in ERA5, which contrasts with 0.03 h decade−1 and 0.14 mm decade−1 for gauge observations, respectively. These results can provide a reference for improving the parameterization scheme of the precipitation triggering mechanism in the process of model simulation.

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Corresponding author: Guocan Wu, gcwu@bnu.edu.cn
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