Climate Dynamics

, Volume 47, Issue 1–2, pp 67–77 | Cite as

Is the interannual variability of summer rainfall in China dominated by precipitation frequency or intensity? An analysis of relative importance

  • Er LuEmail author
  • Ying Ding
  • Bing Zhou
  • Xukai Zou
  • Xianyan Chen
  • Wenyue Cai
  • Qiang Zhang
  • Haishan Chen


The summer rainfall in China has a large interannual variability, which results from the concurrent variations of precipitation frequency and intensity. Using the observed daily precipitation in the 194 stations during recent 62 years, we examine the relative importance of the frequency and intensity in the variability of the rainfall. A simple method, based on linear regression, is used to estimate the relative importance. The products of the change rates of rainfall with respect to frequency and intensity, determined from the regression, and the corresponding standard deviations of the two variables, which reflect their variation scales, are defined to measure the importance of frequency and intensity. To determine the frequency, rainfall amount, and intensity from daily precipitation, we need a threshold to define the “rainy day”. In this study, we use a series of thresholds, ranging from 1 to 30 mm/day. So, while presenting the result of relative importance for each threshold, we also examine how the relative importance varies with the threshold. Results show that for the threshold of 1 mm/day, with which the rainfall may include even the light rains, the variabilities of summer rainfall in most stations are dominated by intensity. With the increase in threshold, the importance of frequency increases, while the importance of intensity decreases. When the threshold reaches 30 mm/day, with which the rainfall includes only moderate-to-heavy rains, the variabilities of the rainfall in all stations are dominated by frequency. Analysis suggests that such a change, in the dominance with the threshold, is reasonable. This reasonability, in turn, supports the reliability and robustness of the method.


Interannual variability Seasonal rainfall Precipitation frequency Precipitation intensity Relative importance Dominance analysis 



This study was supported by the National Basic Research (973) Program of China (Grant 2012CB955900), the China Special Fund for Meteorological Research in the Public Interest (Major projects) (Grant GYHY201506001), the National Natural Science Foundation of China (Grants 41275092, 41230422 and 41230528), the Sino-US Center for Weather & Climate Extremes (CWCE) at Nanjing University of Information Science and Technology, and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). The anonymous reviewers and Dr. Ben Kirtman, the editor, are thanked for their constructive suggestions that helped improve the manuscript. The precipitation data used in this study were provided by the National Meteorological Center of China Meteorological Administration (NMC/CMA).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Arruda HVD, Pinto HS (1980) An alternative model for dry-spell probability analysis. Mon Weather Rev 108:823–825CrossRefGoogle Scholar
  2. Azen R, Budescu DV (2003) The dominance analysis approach for comparing predictors in multiple regression. Psychol Methods 8:129–148CrossRefGoogle Scholar
  3. Azen R, Budescu DV (2006) Comparing predictors in multivariate regression models: an extension of dominance analysis. J Behav Stat 31:157–180CrossRefGoogle Scholar
  4. Behson SJ (2005) The relative contribution of formal and informal organizational work–family support. J Vocat Behav 66:487–500CrossRefGoogle Scholar
  5. Biasutti M, Yuter SE (2013) Observed frequency and intensity of tropical precipitation from instantaneous estimates. J Geophys Res 118:9534–9551Google Scholar
  6. Bring JA (1996) Geometric approach to compare variables in a regression model. Am Stat 50:57–62Google Scholar
  7. Budescu DV (1993) Dominance analysis: a new approach to the problem of relative importance of predictors in multiple regression. Psychol Bull 114:542–551CrossRefGoogle Scholar
  8. Budescu DV, Azen R (2004) Beyond global measures of relative importance: some insights from dominance analysis. Organ Res Methods 7:341–350CrossRefGoogle Scholar
  9. Chen CA, Chou C, Chen CT (2012) Regional perspective on mechanisms for tropical precipitation frequency and intensity under global warming. J Clim 25:8487–8501CrossRefGoogle Scholar
  10. Chou C, Chen CA, Tan PH, Chen KT (2012) Mechanisms for global warming impacts on precipitation frequency and intensity. J Clim 25:3291–3306CrossRefGoogle Scholar
  11. Deng Y, Bowman KP, Jackson C (2007) Differences in rain rate intensities between TRMM observations and community atmosphere model simulations. Geophys Res Lett 34:L01808. doi: 10.1029/2006GL027246 Google Scholar
  12. Gershunov A, Cayan DR (2003) Heavy daily precipitation frequency over the contiguous United States: sources of climatic variability and seasonal predictability. J Clim 16:2752–2765CrossRefGoogle Scholar
  13. Green PE, Carroll JD, DeSarbo WS (1978) A new measure of predictor variable importance in multiple regression. J Mark Res 15:356–360CrossRefGoogle Scholar
  14. Groisman PY, Knight RW, Karl TR (2012) Changes in intense precipitation over the central United States. J Hydrometeorol 13:47–66CrossRefGoogle Scholar
  15. Gromping U (2007) Estimators of relative importance in linear regression based on variance decomposition. Am Stat 61:139–147CrossRefGoogle Scholar
  16. Gutowski WJ, Takle ES, Kozak KA, Patton JC, Arritt RW, Christensen JH (2007) A possible constraint on regional precipitation intensity changes under global warming. J Hydrometeorol 8:1382–1396CrossRefGoogle Scholar
  17. Hoffman PJ (1960) The paramorphic representation of clinical judgment. Psychol Bull 57:116–131CrossRefGoogle Scholar
  18. Hua WJ, Chen HS (2011) Response of land surface processes to global warming and its possible mechanism based on CMIP3 multi-model ensembles. Chin J Atmos Sci 35:121–133 (in Chinese) Google Scholar
  19. Karl TR, Knight RW (1998) Secular trends of precipitation amount, frequency, and intensity in the United States. Bull Am Meteorol Soc 79:231–241CrossRefGoogle Scholar
  20. Kiran C, Krishnamurthy B, Lall U, Kwon HH (2009) Changing frequency and intensity of rainfall extremes over India from 1951 to 2003. J Clim 22:4737–4746CrossRefGoogle Scholar
  21. Krasikova D, Lebreton JM (2011) Estimating the relative importance of variables in multiple regression models. Int Rev Ind Organ Psychol 26:174–192Google Scholar
  22. Li HJ (2012) The analysis of variation characteristics and cause of drought-wetness over Tarim River Basin in recent 50a. PhD Dissertation, pp 115, Nanjing University of Information Science and Technology (in Chinese)Google Scholar
  23. Liebmann B, Jones C, Carvalho LMV (2001) Interannual variability of daily extreme precipitation events in the State of São Paulo, Brazil. J Clim 14:208–218CrossRefGoogle Scholar
  24. Liu BH, Henderson M, Xu M, Zhang YD (2011) Observed changes in precipitation on the wettest days of the year in China, 1960–2000. Int J Climatol 31:487–503CrossRefGoogle Scholar
  25. Lu E, Chan JCL (1999) A unified monsoon index for South China. J Clim 12:2375–2385CrossRefGoogle Scholar
  26. Lu E, Ding YH, Murakami M, Takahashi K (1998) Nature of precipitation and activity of cumulus convection during the 1991 Meiyu season of the Changjiang-Huaihe River basin. Acta Meteorologica Sinica 12:75–91Google Scholar
  27. Lu E, Takle ES, Manoj J (2010) The relationships between climatic and hydrological changes in the upper Mississippi River basin: A SWAT and multi-GCM study. J Hydrometeorol 11:437–451CrossRefGoogle Scholar
  28. Lu E, Luo YL, Zhang RH, Wu QX, Liu LP (2011) Regional atmospheric anomalies responsible for the 2009–2010 severe drought in China. J Geophys Res 116:D21114. doi: 10.1029/2011JD015706 CrossRefGoogle Scholar
  29. Lu E, Cai WY, Jiang ZH, Zhang Q, Zhang CJ, Higgins RW, Halpert MS (2013) The day-to-day monitoring of the 2011 severe drought in China. Clim Dyn. doi: 10.1007/s00382-013-1987-2 Google Scholar
  30. Lu E, Zeng YT, Luo YL, Ding Y, Zhao W, Liu SY, Gong LQ, Jiang Y, Jiang ZH, Chen HS (2014) Changes of summer precipitation in China: the dominance of frequency and intensity and linkage with changes in moisture and air temperature. J Geophys Res 119:12575–12587. doi: 10.1002/2014JD022456 Google Scholar
  31. Michael TB, Frederick LO (2011) Exploratory regression analysis: a tool for selecting models and determining predictor importance. Psychon Soc 8:93–98Google Scholar
  32. Mo KC, Berbery EH (2011) Drought and persistent wet spells over South America based on observations and the U.S. CLIVAR drought experiments. J Clim 24:1801–1820CrossRefGoogle Scholar
  33. Nandargi S, Mulye SS (2012) Relationships between rainy days, mean daily intensity, and seasonal rainfall over the Koyna Catchment during 1961–2005. Sci World J 2012:894313CrossRefGoogle Scholar
  34. Nandintsetseg B, Greene JS, Goulden CE (2007) Trends in extreme daily precipitation and temperature near lake Hövsgöl, Mongolia. Int J Climatol 27:341–347CrossRefGoogle Scholar
  35. Peña M, Douglas MW (2002) Characteristics of wet and dry spells over the Pacific side of central America during the rainy season. Mon Weather Rev 130:3054–3073CrossRefGoogle Scholar
  36. Piccarreta M, Pasini A, Capolongo D, Lazzari M (2013) Changes in daily precipitation extremes in the Mediterranean from 1951 to 2010: the Basilicata region, southern Italy. Int J Climatol 33:3229–3248CrossRefGoogle Scholar
  37. Pratt JW (1987) Dividing the indivisible: using simple symmetry to partition variance explained. In: Pukilla T, Duntaneu D (eds) Proceedings of second tampere conference in statistics, vol 11, Finland, pp 245–260Google Scholar
  38. Ratan R, Venugopal V (2013) Wet and dry spell characteristics of global tropical rainfall. Water Resour Res 49:3830–3841CrossRefGoogle Scholar
  39. Reiser H, Kutiel H (2012) The dependence of the annual total on the number of rain-spells and their yield in the Mediterranean. Geografiska Annaler: Series A. Phys Geogr 94:285–299Google Scholar
  40. Rodrigo FS (2010) Changes in the probability of extreme daily precipitation observed from 1951 to 2002 in the Iberian Peninsula. Int J Climatol 30:1512–1525Google Scholar
  41. Schmidli J, Frei C (2005) Trends of heavy precipitation and wet and dry spells in Switzerland during the 20th century. Int J Climatol 25:753–771CrossRefGoogle Scholar
  42. Seager R, Naik N, Vogel L (2012) Does global warming cause intensified interannual hydroclimate variability? J Clim 25:3355–3372CrossRefGoogle Scholar
  43. Singh N, Ranade A (2010) The wet and dry spells across India during 1951–2007. J Hydrometeorol 11:26–45CrossRefGoogle Scholar
  44. Sun Y, Solomon S, Dai A, Portmann RW (2007) How often will it rain? J Clim 20:4801–4818CrossRefGoogle Scholar
  45. Suppiah R, Hennessy KJ (1998) Trends in total rainfall, heavy rain events and number of dry days in Australia, 1910–1990. Int J Climatol 18:1141–1164CrossRefGoogle Scholar
  46. Tank AMGK, Können GP (2003) Trends in indices of daily temperature and precipitation extremes in Europe, 1946–99. J Clim 16:3665–3680CrossRefGoogle Scholar
  47. Teixeira MS, Satyamurty P (2011) Trends in the frequency of intense precipitation events in southern and southeastern Brazil during 1960–2004. J Clim 24:1913–1921CrossRefGoogle Scholar
  48. Timm OE, Diaz HF, Giambelluca TW, Takahashi M (2011) Projection of changes in the frequency of heavy rain events over Hawaii based on leading Pacific climate modes. J Geophys Res 116:D4Google Scholar
  49. Ward JH (1969) Partitioning of variance and contribution or importance of a variable: a visit to a graduate seminar. Am Educ Res J 6:467–474Google Scholar
  50. Zhai PM, Zhang XB, Wan H, Pan XH (2005) Trends in total precipitation and frequency of daily precipitation extremes over China. J Clim 18:1096–1108CrossRefGoogle Scholar
  51. Zhou TJ, Yu RC, Chen HM, Dai AG, Pan Y (2008) Summer precipitation frequency, intensity, and diurnal cycle over China: a comparison of satellite data with rain gauge observations. J Clim 21:3997–4010CrossRefGoogle Scholar
  52. Zolina O, Simmer C, Belyaev K, Kapala A, Gulev S (2009) Improving estimates of heavy and extreme precipitation using daily records from European rain gauges. J Hydrometeorol 10:701–716CrossRefGoogle Scholar
  53. Zolina O, Simmer C, Belyaev K, Gulev SK, Koltermann P (2013) Changes in the duration of European wet and dry spells during the last 60 years. J Clim 26:2022–2047CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Er Lu
    • 1
    Email author
  • Ying Ding
    • 1
  • Bing Zhou
    • 2
  • Xukai Zou
    • 2
  • Xianyan Chen
    • 2
  • Wenyue Cai
    • 2
  • Qiang Zhang
    • 2
  • Haishan Chen
    • 1
  1. 1.Key Laboratory of Meteorological Disaster of Ministry of EducationNanjing University of Information Science and TechnologyNanjingChina
  2. 2.National Climate CenterCMABeijingChina

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