An evaluation of the consistency of extremes in gridded precipitation data sets

  • Ben TimmermansEmail author
  • Michael Wehner
  • Daniel Cooley
  • Travis O’Brien
  • Harinarayan Krishnan


Noting a strong imperative to understand precipitation extremes, and that considerable uncertainty affects observational data sets, this paper compares the representation of extremes in a number of widely used daily gridded products, derived from rain gauge data, satellite retrieval and reanalysis for the conterminous United States. Analysis is based upon the concept of “tail dependence” arising in multivariate extreme value theory, and we infer the level of temporal dependence in the joint tail of the precipitation probability distribution for pairwise comparisons of products. In this way, we consider the range of products more like an ensemble and examine the relationships between members, and do not attempt to define, or compare products to, some ground truth. Linear correlation between products is also computed. Considerable discrepancy between groups of products, both annually and seasonally, is linked to source data and complex terrain. In particular, products based on rain gauge data showed remarkable similarity, but differed considerably, showing almost total loss of extremal dependence during DJF in mountainous regions, when compared with satellite products. Additionally, simulated re-forecasts revealed reasonable temporal agreement with large scale generated extremes. The diversity and extent of discrepancies identified across all products raises important questions about their use, and we urge caution, particularly for products derived from satellite data.


Precipitation Extremes Gridded products Extreme value theory Tail dependence Comparison 



This material is based upon work supported by the Regional and Global Climate Modeling Program of the US Department of Energy, Office of Science, Office of Biological and Environmental Research, under contract number DE-AC02-05CH11231. Calculations were performed at the National Energy Research Supercomputing Center (NERSC) at the Lawrence Berkeley National Laboratory where the data from these simulations are archived and available from the authors. Cooley’s work on this project was partially supported by the project NSF-DMS 1243102.

Supplementary material

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Supplementary material 1 (pdf 14483 KB)


  1. Adler RF, Huffman GJ, Chang A, Ferraro R, Xie PP, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4:1147–1167CrossRefGoogle Scholar
  2. Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Tank AMGK, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour A, Kumar KR, Revadekar J, Griffiths G, Vincent L, Stephenson DB, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai P, Rusticucci M, Vazquez-Aguirre JL (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res 111:1–22Google Scholar
  3. Ashouri H, Hsu KL, Sorooshian S, Braithwaite DK, Knapp KR, Cecil LD, Nelson BR, Prat OP (2015) PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull Am Meteorol Soc January:69–83CrossRefGoogle Scholar
  4. Auer I, Bohm R, Jurkovic A, Lipa W, Orlik A, Potzmann R, Schoner W, Ungersbock M, Matull C, Briff K, Jones P, Efthymiadis D, Brunetti M, Nanni T, Maugeri M, Mercalli L, Mestre O, Moisselin JM, Begert M, Muller-Westermeier G, Kveton V, Bochnicek O, Stastny P, Lapin M, Szalai S, Szentimrey T, Cegnar T, Dolinar M, Gajic-Capka M, Majstorovic KZZ, Nieplova E (2007) HISTALP-historical instrumental climatological surface time series of the Greater Alpine Region. Int J Climatol 27:17–46CrossRefGoogle Scholar
  5. Beck HE, van Dijk AIJM, Levizzani V, Schellekens J, Miralles DG, Martens B, de Roo A (2017) MSWEP: 3-hourly 0.25 global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrol Earth Syst Scince 21:589–615CrossRefGoogle Scholar
  6. Behnke R, Vavrus S, Allstadt A, Albright T, Thogmartin WE, Radeloff VC (2016) Evaluation of downscaled, gridded climate data for the conterminous United States. Ecol Appl 26:1338–1351CrossRefGoogle Scholar
  7. Behrangi A, Khakbaz B, Jaw TC, AghaKouchak A, Hsu K, Sorooshian S (2011) Hydrologic evaluation of satellite precipitation products over a mid-size basin. J Hydrol 397:225–237CrossRefGoogle Scholar
  8. Blacutt LA, Herdies DL, de Goncalves LGG, Vila DA, Andrade M (2015) Precipitation comparison for the CFSR, MERRA, TRMM3B42 and combined scheme datasets in Bolivia. Atmos Res 163:117–131CrossRefGoogle Scholar
  9. Boers N, Bookhagen B, Marwan N, Kurths J (2016) Spatiotemporal characteristics and synchronization of extreme rainfall in South America with focus on the Andes mountain range. Clim Dyn 46:601–617CrossRefGoogle Scholar
  10. Chen M, Shi W, Xie P, Silva VBS, Kousky VE, Higgins RW, Janowiak JE (2008) Assessing objective techniques for gauge-based analyses of global daily precipitation. J Geophys Res Atmos 113:1854–1872Google Scholar
  11. Chen S, Hong Y, Cao Q, Gourley JJ, Kirstetter PE, Yong B, Tian Y, Zhang Z, Shen Y, Hu J, Hardy J (2013a) Similarity and difference of the two successive v6 and v7 trmm multisatellite precipitation analysis performance over china. J Geophys Res Atmos 118:13060–13074CrossRefGoogle Scholar
  12. Chen Y, Ebert EE, Walsh KJE, Davidson NE (2013b) Evaluation of TRMM 3B42 precipitation estimates of tropical cyclone rainfall using PACRAIN data. J Geophys Res Atmos 118:2184–2196CrossRefGoogle Scholar
  13. Chvila B, Sevruk B, Ondras M (2005) The wind-induced loss of thunderstorm precipitation measurements. Atmos Res 77:29–38CrossRefGoogle Scholar
  14. Coles S (2001) An introduction to statistical modeling of extreme values. Springer, BerlinCrossRefGoogle Scholar
  15. Coles S, Heffernan J, Tawn J (1999) Dependence measures for extreme value analyses. Extremes 2:339–365CrossRefGoogle Scholar
  16. Conti FL, Hsu KL, Noto LV, Sorooshian S (2014) Evaluation and comparison of satellite precipitation estimates with reference to a local area in the mediterranean sea. Atmos Res 138:189–204CrossRefGoogle Scholar
  17. Contractor S, Alexander LV, Donat MG, Herold N (2015) How well do gridded datasets of observed daily precipitation compare over Australia? Adv Meteorol 2015:1–15CrossRefGoogle Scholar
  18. Cressie N (1993) Statistics for spatial data, Revised edn. Wiley, OxfordGoogle Scholar
  19. Dalhaus T, Finger R (2016) Can gridded precipitation data and phenological observations reduce basis risk of weather index-based insurance? Weather Clim Soc 8:409–419CrossRefGoogle Scholar
  20. Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, Curtis J, Pasteris PP (2008) Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int J Climatol 15:2031CrossRefGoogle Scholar
  21. Daly C, Slater ME, Roberti JA, Laseter SH Jr, Swift LW (2017) High-resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset. Int J Climatol 37:124CrossRefGoogle Scholar
  22. Davison A, Huser R (2015) Statistics of extremes. Annu Rev Stat Appl 2:203–235CrossRefGoogle Scholar
  23. Davison AC, Smith RL (1990) Models for exceedances over high thresholds. J R Stat Soc Ser B 52:393–442Google Scholar
  24. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Holm EV, Isaksen L, Kallberg P, Kohler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thepaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
  25. Easterling D, Kunkel K, Arnold J, Knutson T, LeGrande A, Leung L, Vose R, Waliser D, Wehner M (2017) Climate science special report: fourth national climate assessment, volume I, US Global Change Research Program, chap Precipitation change in the United States, pp 207–230Google Scholar
  26. Ferro CAT, Stephenson DB (2011) Extremal dependence indices: Improved verification measures for deterministic forecasts of rare binary events. Weather Forecast 26:699–713CrossRefGoogle Scholar
  27. Fick S, Hijmans R (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302CrossRefGoogle Scholar
  28. Gandin LS, Hardin R (1965) Objective analysis of meteorological fields. Israel program for scientific translations JerusalemGoogle Scholar
  29. Gervais M, Gyakum JR, Atallah E, Tremblay LB, Neale RB (2014a) How well are the distribution and extreme values of daily precipitation over North America represented in the Community Climate System Model? A comparison to reanalysis, satellite, and gridded station data. J Clim 27:5219–5239CrossRefGoogle Scholar
  30. Gervais M, Tremblay LB, Gyakum JR, Atallah E (2014b) Representing extremes in a daily gridded precipitation analysis over the United States: impacts of station density, resolution, and gridding methods. J Clim 27:5201–5218CrossRefGoogle Scholar
  31. Gleckler PJ, Taylor KE, Doutriaux C (2008) Performance metrics for climate models. J Geophys Res 113:1–20CrossRefGoogle Scholar
  32. Greene JS, Klatt M, Morrissey M, Postawko S (2008) The comprehensive pacific rainfall database. J Atmos Ocean Technol 25:71–82CrossRefGoogle Scholar
  33. Groisman PY, Legates DR (1994) The accuracy of united states precipitation data. Bull Am Meteorol Soc 1994:215–227CrossRefGoogle Scholar
  34. Groisman PYA, Karl TR, Easterling DR, Knight RW, Jamason PF, Hennessy KJ, Suppiah R, Page CM, Wibig J, Fortuniak K, Razuvaev VN, Douglas A, Forland E, Zhai PM (1999) Changes in the probability of heavy precipitation: important indicators of climatic change. In: Weather and climate extremes. Springer, DordrechtGoogle Scholar
  35. Haylock MR, Hofstra N, Tank AMGK, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res 113:1–12CrossRefGoogle Scholar
  36. Henn B, Newman AJ, Livneh B, Daly C, Lundquist JD (2017) An assessment of differences in gridded precipitation datasets in complex terrain. J Hydrol (in press) Google Scholar
  37. Herrera S, Fernandez J, Gutierrez JM (2016) Update of the Spain02 gridded observational dataset for EURO-CORDEX evaluation: assessing the effect of the interpolation methodology. Int J Climatol 36:900–908CrossRefGoogle Scholar
  38. Hill C, DeLuca C, Balaji V, Suarez MJ, da Silva A (2004) The architecture of the earth system modeling framework. Comput Sci Eng 6(1):18–28CrossRefGoogle Scholar
  39. Hofstra N, Haylock M, New M, Jones P, Frei C (2008) Comparison of six methods for the interpolation of daily, European climate data. J Geophys Res Atmos 113:1–19CrossRefGoogle Scholar
  40. Holder C, Boyles R, Syed A, Niyogi D, Raman S (2006) Comparison of collocated automated (NCECONet) and manual (COOP) climate observations in North Carolina. J Atmos Ocean Technol 21:671–682CrossRefGoogle Scholar
  41. Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55CrossRefGoogle Scholar
  42. Huser R, Opitz T, Thibaud E (2017) Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures. Spat Stat 21:166–186CrossRefGoogle Scholar
  43. Iguchi T, Kozu T, Kwiatkowski J, Meneghini R, Awaka J, Okamoto K (2009) Uncertainties in the rain profiling algorithm for the trmm precipitation radar. J Meteorol Soc Jpn 87A:1–30CrossRefGoogle Scholar
  44. Isotta FA, Frei C, Weilguni V, Tadic MP, Lassegues P, Rudolf B, Pavan V, Cacciamani C, Antolini G, Ratto SM, Munari M, Micheletti S, Bonati V, Lussana C, Ronchi C, Panettieri E, Marigo G, Vertacnik G (2014) The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data. Int J Climatol 34:1657–1675CrossRefGoogle Scholar
  45. Jiang S, Ren L, Hong Y, Yong B, Yang X, Yuan F, Ma M (2012) Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method. J Hydrol 452:213–225CrossRefGoogle Scholar
  46. Jolliffe IT, Stephenson DB (2012) Forecast verification: a practitioner’s guide in atmospheric science. Wiley, OxfordGoogle Scholar
  47. Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503CrossRefGoogle Scholar
  48. King AD, Alexander LV, Donat MG (2013) The efficacy of using gridded data to examine extreme rainfall characteristics: a case study for Australia. Int J Climatol 33:2376–2387CrossRefGoogle Scholar
  49. Kochendorfer J, Rasmussen R, Wolff M, Baker B, Hall ME, Meyers T, Landolt S, Jachcik A, Isaksen K, Braekkan R, Leeper R (2017) The quantification and correction of wind-induced precipitation measurement errors. Hydrol Earth Syst Sci 21:1973–1989CrossRefGoogle Scholar
  50. Kotlarski S, Keuler K, Christensen OB, Colette A, Deque M, Gobiet A, Goergen K, Jacob D, Luthi D, van Meijgaard E, Nikulin G, Schar C, Teichmann C, Vautard R, Warrach-Sagi K, Wulfmeyer V (2014) Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geosci Model Dev 7:1297–1333CrossRefGoogle Scholar
  51. Krishnamurthy V, Shukla J (2000) Intraseasonal and interannual variability of rainfall over India. J Clim 13:4366–4377CrossRefGoogle Scholar
  52. Kuhn G, Khan S, Ganguly AR, Branstetter ML (2007) Geospatial-temporal dependence among weekly precipitation extremes with applications to observations and climate model simulations in South America. Adv Water Resour 30:2401–2423CrossRefGoogle Scholar
  53. Kunkel KE, Brooks TRKH, Kossin J, Lawrimore JH, Arndt D, Bosart L, Changnon D, Cutter SL, Doesken N, Emanuel K, Groisman PY, Katz RW, Knutson T, O’Brien J, Paciorek CJ, Peterson TC, Redmond K, Robinson D, Trapp J, Vose R, Weaver S, Wehner M, Wolter K, Wuebbles D (2013) Monitoring and understanding trends in extreme storms: State of knowledge. Bull Am Meteorol Soc April:499–514CrossRefGoogle Scholar
  54. Lambert FH, Stott PA, Allen MR, Palmer MA (2004) Detection and attribution of changes in 20th century land precipitation. Geophys Res Lett 31:1–4CrossRefGoogle Scholar
  55. Ledford AW, Tawn JA (1996) Statistics for near independence in multivariate extreme values. Biometrika 83:169–187CrossRefGoogle Scholar
  56. Ledford AW, Tawn JA (1997) Modelling dependence within joint tail regions. J R Stat Soc Ser B 59:475–499CrossRefGoogle Scholar
  57. Leeper RD, Rennie J, Palecki MA (2015) Observational perspectives from US Climate Reference Network (USCRN) and Cooperative Observer Program (COOP) network: temperature and precipitation comparison. J Atmos Ocean Technol 32:703–721CrossRefGoogle Scholar
  58. Lerch S, Thorarinsdottir TL, Ravazzolo F, Gneiting T (2017) Forecaster’s dilemma: extreme events and forecast evaluation. Stat Sci 32:106–127CrossRefGoogle Scholar
  59. Livneh B, Rosenberg EA, Lin C, Nijssen B, Mishra V, Andradis KM, Maurer EP, Lettenmaier DP (2013) A long-term hydrologically based dataset of land surface fluxes and states for the conterminous united states: update and extensions. J Clim 27:478–486Google Scholar
  60. Lundquist JD, Minder JR, Neiman PJ, Sukovich E (2010) Relationships between barrier jet heights, orographic precipitation gradients, and streamflow in the northern Sierra Nevada. J Hydrometeorol 11:1141–1156CrossRefGoogle Scholar
  61. Ma Y, Tang G, Long D, Yong B, Zhong L, Wan W, Hong Y (2016) Similarity and error intercomparison of the GPM and its predecessor-TRMM multisatellite precipitation analysis using the best available hourly gauge network over the tibetan plateau. Remote Sens 8:569CrossRefGoogle Scholar
  62. Maggioni V, Meyers PC, Robinson MD (2016) A review of merged high-resolution satellite precipitation product accuracy during the tropical rainfall measuring mission (TRMM) era. J Hydrometerol 17:1101–1117CrossRefGoogle Scholar
  63. Maurer EP, Wodd AW, Adam JC, Lettenmaier DP, Nijssen B (2002) A long-term hydrologically based dataset of land surface fluxes and states for the conterminous united states. J Clim 15:3237–3251CrossRefGoogle Scholar
  64. McAfee S, Guentchev G, Eischeid J (2014) Reconciling precipitation trends in Alaska: 2 gridded data analyses. J Geophys Res Atmos 119:13820–13837CrossRefGoogle Scholar
  65. Mehran A, AghaKouchak A, Phillips TJ (2014) Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations. J Geophys Res Atmos 119:1695–1707CrossRefGoogle Scholar
  66. Menne MJ, Durre I, Vose RS, Gleason BE, Houston TG (2012) An overview of the global historical climatology network-daily database. J Atmos Ocean Technol 29:897–910CrossRefGoogle Scholar
  67. Mesinger F, DiMego G, Kalnay E, Mitchell K, Shafran PC, Ebisuzaki W, Jovic D, Woollen J, Rogers E, Berbery EH, Ek MB, Fan Y, Grumbine R, Higgins W, Li H, Lin Y, Manikin G, Parrish D, Shi W (2006) North american regional reanalysis. Bull Am Meteorol Soc March:343–360CrossRefGoogle Scholar
  68. Miao C, Ashouri H, Hsu KL, Sorooshian S, Duan Q (2015) Evaluation of the PERSIANN-CDR daily rainfall estimates in capturing the behavior of extreme precipitation events over china. Bull Am Meteorol Soc January:69–83Google Scholar
  69. Michaelides S, Levizzani V, Anagnostou E, Bauer P, Kasparis T, Lane J (2009) Precipitation: measurement, remote sensing, climatology and modeling. Atmos Res 94:512–533CrossRefGoogle Scholar
  70. Neiting TG, Raftery AE (2007) Strictly proper scoring rules, prediction and estimation. J Am Stat Assoc 102:359–378CrossRefGoogle Scholar
  71. Newman AJ, Clark MP, Craig J, Nijssen B, Wood A, Gutmann E (2015) Gridded ensemble precipitation and temperature estimates over the contiguous United States. UCAR/NCAR-CISL-CDP, BoulderGoogle Scholar
  72. O’Brien TA, Collins WD, Kashinath K, Rubel O, Byna S, Gu J, Krishnan H, Ullrich PA (2016) Resolution dependence of precipitation statistical fidelity in hindcast simulations. J Adv Model Earth Syst 8:976–990CrossRefGoogle Scholar
  73. Ostrouchov G, Chen WC, Schmidt D, Patel P (2012) Programming with big data in RGoogle Scholar
  74. Oyler JW, Nicholas RE (2017) Time of observation adjustments to daily station precipitation may introduce undesired statistical issues. Int J Climatol 38:e364–e377CrossRefGoogle Scholar
  75. Pena-Arancibia JL, van Dijk AIJM, Renzullo LJ, Mulligan M (2013) Evaluation of precipitation estimation accuracy in reanalyses, satellite products, and an ensemble method for regions in australia and south and east asia. J Hydrometeorol 14:1323–1333CrossRefGoogle Scholar
  76. Rauthe M, Steiner H, Riediger U, Mazurkiewicz A, Gratzki A (2013) A central european precipitation climatology—part I: generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorol Z 22:235–256CrossRefGoogle Scholar
  77. Rienecker MM, Suarez MJ, Gelaro R, Todling R, Bacmeister J, Liu E, Bosilovich MG, Schubert SD, Takacs L, Kim GK, Bloom S, Chen J, Collins D, Conaty A, da Silva A, Gu W, Joiner J, Koster RD, Lucchesi R, Molod A, Owens T, Pawson S, Pegion P, Redder CR, Reichle R, Robertson FR, Ruddick AG, Sienkiewicz M, Woollen J (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J Clim 24:3624–3648CrossRefGoogle Scholar
  78. Roe GH (2005) Orographic precipitation. Annu Rev Earth Planet Sci 33:645–671CrossRefGoogle Scholar
  79. Salio P, Hobouchian MP, Skabar YG, Vila D (2015) Evaluation of high-resolution satellite precipitation estimates over southern south america using a dense rain gauge network. Atmos Res 163:146–161CrossRefGoogle Scholar
  80. Sanderson B, Wehner M, Knutti R (2017) Skill and independence weighting for multi-model assessments. Geosci Model Dev (in review) Google Scholar
  81. Schar C, Ban N, Fischer EM, Rajczak J, Schmidli J, Frei C, Giorgi F, Karl TR, Kendon EJ, Tank AMGK, O’Gorman PA, Sillmann J, Zhang X, Zwiers FW (2016) Percentile indices for assessing changes in heavy precipitation events. Clim Change 2016:137Google Scholar
  82. Schneider U, Finger P, Meyer-Christoffer A, Rustemeier E, Ziese M, Becker A (2017) Evaluating the hydrological cycle over land using the newly-corrected precipitation climatology from the global precipitation climatology centre (GPCC). Atmosphere 8:52CrossRefGoogle Scholar
  83. Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 23rd ACM national conference, ACM, pp 517–524Google Scholar
  84. Stephenson DB, Casati B, Ferro CAT, Wilson CA (2008) The extreme dependency score: a non-vanishing measure for forecasts of rare events. Meteorol Appl 15:41–50CrossRefGoogle Scholar
  85. Sunyer MA, Sorup HJD, Christensen OB, Madsen H, Rosbjerg D, Mikkelsen PS, Arnbjerg-Nielsen K (2013) On the importance of observational data properties when assessing regional climate model performance of extreme precipitation. Hydrol Earth Syst Sci 17:4323–4337CrossRefGoogle Scholar
  86. Tapiador FJ, Turk F, Petersen W, Hou AY, Garcia-Ortega E, Machado LA, Angelis CF, Salio P, Kidd C (2012) Global precipitation measurement: methods, datasets and applications. Atmos Res 104–105:70–97CrossRefGoogle Scholar
  87. Thornton PE, Running SW, White MA (1997) Generating surfaces of daily meteorological variables over large regions of complex terrain. J Hydrol 190:214–251CrossRefGoogle Scholar
  88. Thornton PE, Thornton MM, Mayer BW, Wei Y, Devarakonda R, Vose R, Cook R (2016) Daymet: daily surface weather data on a 1-km grid for north america, version:3Google Scholar
  89. Timmermans B, Stone D, Wehner M, Krishnan H (2017) Impact of tropical cyclones on modeled extreme wind-wave climate. Geophys Res Lett 44:1393–1401CrossRefGoogle Scholar
  90. Uppala SM, P W Kallberg AJ (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012CrossRefGoogle Scholar
  91. Viney NR, Bates BC (2004) It never rains on Sunday: the prevalence and implications of untagged multi-day rainfall accumulations in the Australian high quality data set. Int J Climatol 24:1171–1192CrossRefGoogle Scholar
  92. Wadsworth JL, Tawn JA, Davison AC, Elton DM (2017) Modelling across extremal dependence classes. J R Stat Soc Ser B 79:149–175CrossRefGoogle Scholar
  93. Wehner MF (2013) Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections. Clim Dyn 40:59–80CrossRefGoogle Scholar
  94. Wehner MF, Reed KA, Li F, Prabhat BJ, Chen CT, Paciorek C, Gleckler PJ, Sperber KR, Gettelman WDCA, Jablonowski C (2014) The effect of horizontal resolution on simulation quality in the community atmospheric model, CAM5.1. J Adv Model Earth Syst 6:980–997CrossRefGoogle Scholar
  95. Weller GB, Cooley DS, Sain SR (2012) An investigation of the pineapple express phenomenon via bivariate extreme value theory. Environmetrics 23:420–439CrossRefGoogle Scholar
  96. Woldemeskel FM, Sivakumar B, Sharma A (2013) Merging gauge and satellite rainfall with specification of associated uncertainty across Australia. J f Hydrol 499:167–176CrossRefGoogle Scholar
  97. Xie P, Chen M, Yang S, Yatagai A, Hayasaka T, Fukushima Y, Liu C (2007) A gauge-based analysis of daily precipitation over East Asia. J Hydrometeorol 8:607–626CrossRefGoogle Scholar
  98. Xie P, Joyce R, Wu S, Yoo SH, Yarosh Y, Sun F, Lin R (2017) Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998. J Hydrometeorol 18:1617–1641CrossRefGoogle Scholar
  99. Xue X, Hong Y, Limayec AS, Gourley JJ, Huffman GJ, Khana SI, Dorjif C, Chen S (2013) Statistical and hydrological evaluation of TRMM-based multi-satellite precipitation analysis over the Wangchu basin of Bhutan: are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? J Hydrol 499:91–99CrossRefGoogle Scholar
  100. Yatagai A, Kamiguchi K, Arakawa O, Hamada A, Yasutomi N, Kitoh A (2012) APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull Am Meteorol Soc 2012:1401–1415CrossRefGoogle Scholar
  101. Zhang X, Alexander L, Hegerl GC, Jones P, Tank AK, Peterson TC, Trewin B, Zwiers FW (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Clim Change 2:851–870CrossRefGoogle Scholar
  102. Zolina O, Simmer C, Kapala A, Shabanov P, Becker P, Machel H, Gulev S, Groisman P (2014) Precipitation variability and extremes in Central Europe: New view from STAMMEX results. Bull Am Meteorol Soc 2014:995–1002CrossRefGoogle Scholar

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© Regents of the University of California 2019

Authors and Affiliations

  1. 1.Lawrence Berkeley National LaboratoryBerkeleyUSA

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