Journal of Meteorological Research

, Volume 31, Issue 4, pp 678–693 | Cite as

Statistical modeling of CMIP5 projected changes in extreme wet spells over China in the late 21st century



The observed intensity, frequency, and duration (IFD) of summer wet spells, defined here as extreme events with one or more consecutive days in which daily precipitation exceeds a given threshold (the 95th percentile), and their future changes in RCP4.5 and RCP8.5 in the late 21st century over China, are investigated by using the wet spell model (WSM) and by extending the point process approach to extreme value analysis. Wet spell intensity is modeled by a conditional generalized Pareto distribution, frequency by a Poisson distribution, and duration by a geometric distribution, respectively. The WSM is able to realistically model summer extreme rainfall spells during 1961–2005, as verified with observations at 553 stations throughout China. To minimize the impact of systematic biases over China in the global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5), five best GCMs are selected based on their performance to reproduce observed wet spell IFD and average precipitation during the historical period. Furthermore, a quantile–quantile scaling correction procedure is proposed and applied to produce ensemble projections of wet spell IFD and corresponding probability distributions. The results show that in the late 21st century, most of China will experience more extreme rainfall and less low-intensity rainfall. The intensity and frequency of wet spells are projected to increase considerably, while the duration of wet spells will increase but to a much less extent. The IFD changes in RCP8.5 are in general much larger than those in RCP4.5.

Key words

wet spell model extreme value theory bias correction Coupled Model Intercomparison Project Phase 5 


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  1. Acero, F. J., J. A. García, and M. C. Gallego, 2011: Peaks-overthreshold study of trends in extreme rainfall over the Iberian Peninsula. J. Climat,e 24, 1089–1105, doi: 10.1175/2010JCLI3627.1.CrossRefGoogle Scholar
  2. Akima, H., 1978: A method of bivariate interpolation and smooth surface fitting for irregularly distributed data points. ACM Trans. Math. Softw,. 4, 148–159, doi: 10.1145/355780. 355786.CrossRefGoogle Scholar
  3. Alexander, L. V., X. Zhang, T. C. Peterson, et al., 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111(D5), doi: 10.1029/2005JD006290.Google Scholar
  4. Bai, A. J., P. M. Zhai, and X. D. Liu, 2007: Climatology and trends of wet spells in China. Theor. Appl. Climatol., 88, 139–148, doi: 10.1007/s00704-006-0235-7.CrossRefGoogle Scholar
  5. Chen, H. P., 2013: Projected change in extreme rainfall events in China by the end of the 21st century using CMIP5 models. Chinese Sci. Bull., 58, 1462–1472, doi: 10.1007/s11434-012-5612-2.CrossRefGoogle Scholar
  6. Cheng, L. Y., and A. AghaKouchak, 2014: Nonstationary precipitation intensity–duration–frequency curves for infrastructure design in a changing climate. Sci. Rep,. 4, 7093, doi: 10.1038/srep07093.CrossRefGoogle Scholar
  7. Chen, W. L., Z. H. Jiang, and L. Li, 2011: Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. J. Climate, 24, 4741–4756, doi: 10.1175/2011JCLI4102.1.CrossRefGoogle Scholar
  8. Chen, H. P., and J. Q. Sun, 2015: Changes in climate extreme events in China associated with warming. Int. J. Climatol., 35, 2735–2751, doi: 10.1002/joc.4168.CrossRefGoogle Scholar
  9. Coles, S., 2001: An Introduction to Statistical Modeling of Extreme Values: Springer Series in Statistics. Springer, London, 209 pp.CrossRefGoogle Scholar
  10. Ding, Y. G., B. Y. Cheng, and Z. H. Jiang, 2008: A newlydiscovered GPD-GEV relationship together with comparing their models of extreme precipitation in summer. Adv. Atmos. Sci., 25, 507–516, doi: 10.1007/s00376-008-0507-5.CrossRefGoogle Scholar
  11. Fan, L. J., and D. L. Chen, 2016: Trends in extreme precipitation indices across China detected using quantile regression. Atmos. Sci. Lett., 17, 400–406, doi: 10.1002/asl.671.CrossRefGoogle Scholar
  12. Fan, L. J., Z. W. Yan, D. L. Chen, et al., 2015: Comparison between two statistical downscaling methods for summer daily rainfall in Chongqing, China. Int. J. Climatol., 35, 3781–3797, doi: 10.1002/joc.4246.CrossRefGoogle Scholar
  13. Fang, G. H., J. Yang, Y. N. Chen, et al., 2015: Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrol. Earth Syst. Sci., 19, 2547–2559, doi: 10.5194/hess-19- 2547-2015.CrossRefGoogle Scholar
  14. Furrer, E. M., R. W. Katz, M. D. Walter, et al., 2010: Statistical modeling of hot spells and heat waves. Climate Res., 43, 191–205, doi: 10.3354/cr00924.CrossRefGoogle Scholar
  15. Guo, P. W., X. K. Zhang, S. Y. Zhang, et al., 2014: Decadal variability of extreme precipitation days over Northwest China from 1963 to 2012. J. Meteor. Res., 28, 1099–1113, doi: 10.1007/s13351-014-4022-6.CrossRefGoogle Scholar
  16. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T. F., et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.Google Scholar
  17. Jiang, Z. H., W. Li, J. J. Xu, et al., 2015: Extreme precipitation indices over China in CMIP5 models. Part I: Model evaluation. J. Climate, 28, 8603–8619, doi: 10.1175/JCLI-D-15-0099.1.CrossRefGoogle Scholar
  18. Kallache, M., M. Vrac, P. Naveau, et al., 2011: Nonstationary probabilistic downscaling of extreme precipitation. J. Geophys. Res., 116, doi: 10.1029/2010JD014892.Google Scholar
  19. Karl, T. R., N. Nicholls, and A. Ghazi, 1999: Clivar/GCOS/WMO workshop on indices and indicators for climate extremes workshop summary. Climatic Change, 42, 3–7, doi: 10.1023/A:1005491526870.CrossRefGoogle Scholar
  20. Katz, R. W., 2013: Statistical methods for nonstationary extremes. Extremes in a Changing Climate: Detection, Analysis and Uncertainty. AghaKouchak, A., D. Easterling, K. Hsu, et al., Eds., Springer, Netherlands, 15–37.CrossRefGoogle Scholar
  21. Ke, D., and Z. Y. Guan, 2014: Variations in regional mean daily precipitation extremes and related circulation anomalies over central China during boreal summer. J. Meteor. Res., 28, 524–539, doi: 10.1007/s13351-014-3246-9.CrossRefGoogle Scholar
  22. Lau, W. K. M., H. T. Wu, and K. M. Kim, 2013: A canonical response of precipitation characteristics to global warming from CMIP5 models. Geophys. Res. Lett., 40, 3163–3169, doi: 10.1002/grl.50420.CrossRefGoogle Scholar
  23. Li, M. G., Z. Y. Guan, D. C. Jin, et al., 2016: Anomalous circulation patterns in association with two types of daily precipitation extremes over southeastern China during boreal summer. J. Meteor. Res., 30, 183–202, doi: 10.1007/s13351-016-5070-x.CrossRefGoogle Scholar
  24. Li, X., A. Meshgi, and V. Babovic, 2016: Spatio-temporal variation of wet and dry spell characteristics of tropical precipitation in Singapore and its association with ENSO. Int. J. Climatol., 36, 4831–4846, doi: 10.1002/joc.4672.CrossRefGoogle Scholar
  25. Li, Y., W. Cai, and E. P. Campbell, 2005: Statistical modeling of extreme rainfall in southwest western Australia. J. Climate, 18, 852–863, doi: 10.1175/JCLI-3296.1.CrossRefGoogle Scholar
  26. Liang, K., S. Liu, P. Bai, et al., 2015: The Yellow River basin becomes wetter or drier? The case as indicated by mean precipitation and extremes during 1961–2012. Theor. Appl. Climatol., 119, 701–722, doi: 10.1007/s00704-014-1138-7.CrossRefGoogle Scholar
  27. Ma, S. M., and T. J. Zhou, 2015: Observed trends in the timing of wet and dry season in China and the associated changes in frequency and duration of daily precipitation. Int. J. Climatol., 35, 4631–4641, doi: 10.1002/joc.4312.CrossRefGoogle Scholar
  28. Mondal, A., and P. P. Mujumdar, 2015: Modeling non-stationarity in intensity, duration and frequency of extreme rainfall over India. J. Hydrol,. 521, 217–231, doi: 10.1016/j.jhydrol. 2014.11.071.CrossRefGoogle Scholar
  29. Moss, R. H., J. A. Edmonds, K. A. Hibbard, et al., 2010: The next generation of scenarios for climate change research and assessment. Nature, 463, 747–756, doi: 10.1038/nature08823.CrossRefGoogle Scholar
  30. Ou, T. H., D. L. Chen, H. W. Linderholm, et al., 2013: Evaluation of global climate models in simulating extreme precipitation in China. Tellus A, 65, 19799, doi: 10.3402/tellusa.v65i0.19799.CrossRefGoogle Scholar
  31. Qian, X., Q. L. Miao, P. M. Zhai, et al., 2014: Cold-wet spells in mainland China during 1951–2011. Nat. Hazard,s 74, 931–946, doi: 10.1007/s11069-014-1227-z.CrossRefGoogle Scholar
  32. R Core Team, 2016: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL Scholar
  33. She, D. X., J. Xia, J. Y. Song, et al., 2013: Spatio-temporal variation and statistical characteristic of extreme dry spell in Yellow River basin, China. Theor. Appl. Climato,l. 112, 201–213, doi: 10.1007/s00704-012-0731-x.CrossRefGoogle Scholar
  34. Su, B. D., B. Xiao, D. M. Zhu, et al., 2005: Trends in frequency of precipitation extremes in the Yangtze River basin, China: 1960–2003. Hydrological Sciences Journal, 50, 479–492, doi: 10.1623/hysj.50.3.479.65022.CrossRefGoogle Scholar
  35. Sugahara, S., R. P. Da Rocha, and R. Silveira, 2009: Non-stationary frequency analysis of extreme daily rainfall in Sao Paulo, Brazil. Int. J. Climatol,. 29, 1339–1349, doi: 10.1002/joc.1760.CrossRefGoogle Scholar
  36. Sun, J. Q., and J. Ao, 2013: Changes in precipitation and extreme precipitation in a warming environment in China. Chinese Sci. Bull., 58, 1395–1401, doi: 10.1007/s11434-012-5542-z.CrossRefGoogle Scholar
  37. Sun, Y., S. Solomon, A. G. Dai, et al., 2007: How often will it rain? J. Climate, 20, 4801–4818, doi: 10.1175/JCLI4263.1.CrossRefGoogle Scholar
  38. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498, doi: 10.1175/BAMS-D-11-00094.1.CrossRefGoogle Scholar
  39. Teutschbein, C., and J. Seibert, 2012: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. J. Hydrol,. 456–457, 12–29, doi: 10.1016/j.jhydrol.2012.05.052.CrossRefGoogle Scholar
  40. Themeßl, M. J., A. Gobiet, and G. Heinrich, 2012: Empirical–statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Climatic Change, 112, 449–468, doi: 10.1007/s10584-011-0224- 4.CrossRefGoogle Scholar
  41. Tolika, K., and P. Maheras, 2005: Spatial and temporal characteristics of wet spells in Greece. Theor. Appl. Climatol., 81, 71–85, doi: 10.1007/s00704-004-0089-9.CrossRefGoogle Scholar
  42. van der Schrier, G., J. Barichivich, K. R. Briffa, et al., 2013: A scPDSI-based global data set of dry and wet spells for 1901–2009. J. Geophys. Res., 118, 4025–4048, doi: 10.1002/jgrd.50355.Google Scholar
  43. Wan, S. Q., Y. L. Hu, Z. Y. You, et al., 2013: Extreme monthly precipitation pattern in China and its dependence on Southern Oscillation. Int. J. Climatol., 33, 806–814, doi: 10.1002/joc.3466.CrossRefGoogle Scholar
  44. Wang, K., L. Wang, Y. M. Wei, et al., 2013: Beijing storm of July 21, 2012: Observations and reflections. Nat. Hazards, 67, 969–974, doi: 10.1007/s11069-013-0601-6.CrossRefGoogle Scholar
  45. Wang, W. W., W. Zhou, Y. Li, et al., 2015: Statistical modeling and CMIP5 simulations of hot spell changes in China. Climate Dyn., 44, 2859–2872, doi: 10.1007/s00382-014-2287-1.CrossRefGoogle Scholar
  46. Wen, Y. R., L. Xue, Y. Li, et al., 2015: Interaction between Typhoon Vicente (1208) and the western Pacific subtropical high during the Beijing extreme rainfall of 21 July 2012. J. Meteor. Res., 29, 293–304, doi: 10.1007/s13351-015-4097-8.CrossRefGoogle Scholar
  47. Wu, H., P. M. Zhai, and Y. Chen, 2016: A comprehensive classification of anomalous circulation patterns responsible for persistent precipitation extremes in South China. J. Meteor. Res., 30, 483–495, doi: 10.1007/s13351-016-6008-z.CrossRefGoogle Scholar
  48. Wu, J., and X. J. Gao, 2013: A gridded daily observation dataset over China region and comparison with the other datasets. Chinese J. Geophys., 56, 1102–1111. (in Chinese)Google Scholar
  49. Xie, J. L., 2002: Forecast of persistent rainy weather in spring in Zhanjiang. Guangdong Meteorology, 1, 1–4. (in Chinese)Google Scholar
  50. Xu, Y., X. J. Gao, Y. Shen, et al., 2009: A daily temperature dataset over China and its application in validating a RCM simulation. Adv. Atmos. Sci., 26, 763–772, doi: 10.1007/s00376- 009-9029-z.CrossRefGoogle Scholar
  51. You, Q. L., S. C. Kang, E. Aguilar, et al., 2011: Changes in daily climate extremes in China and their connection to the large scale atmospheric circulation during 1961–2003. Climate Dyn., 36, 2399–2417, doi: 10.1007/s00382-009-0735-0.CrossRefGoogle Scholar
  52. Yu, M. X., Q. F. Li, M. J. Hayes, et al., 2014: Are droughts becoming more frequent or severe in China based on the Standardized Precipitation Evapotranspiration Index: 1951–2010? Int. J. Climatol., 34, 545–558, doi: 10.1002/joc.3701.CrossRefGoogle Scholar
  53. Zhai, P. M., X. B. Zhang, H. Wan, et al., 2005: Trends in total precipitation and frequency of daily precipitation extremes over China. J. Climate, 18, 1096–1108, doi: 10.1175/JCLI-3318.1.CrossRefGoogle Scholar
  54. Zhang, Q., V. P. Singh, J. F. Li, et al., 2011: Analysis of the periods of maximum consecutive wet days in China. J. Geophys. Res., 116, D23106, doi: 10.1029/2011JD016088.CrossRefGoogle Scholar
  55. Zhao, G. J., G. Hörmann, N. Fohrer, et al., 2009: Spatial and temporal characteristics of wet spells in the Yangtze River basin from 1961 to 2003. Theor. Appl. Climatol., 98, 107–117, doi: 10.1007/s00704-008-0099-0.CrossRefGoogle Scholar
  56. Zhou, B. T., Q. H. Wen, Y. Xu, et al., 2014: Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles. J. Climate, 27, 6591–6611, doi: 10.1175/JCLI-D-13-00761.1.CrossRefGoogle Scholar
  57. Zhou, T. J., X. L. Chen, L. Dong, et al., 2014: Chinese contribution to CMIP5: An overview of five Chinese models’ performances. J. Meteor. Res,. 28, 481–509, doi: 10.1007/s13351-014-4001-y.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science & TechnologyNanjingChina
  2. 2.School of Mathematics and StatisticsNanjing University of Information Science & TechnologyNanjingChina
  3. 3.Business Intelligence & Data AnalyticsWestern PowerAustralia

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