Spatiotemporal Changes in Precipitation Extremes over Canada and Their Teleconnections to Large-Scale Climate Patterns

Yang Yang Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada

Search for other papers by Yang Yang in
Current site
Google Scholar
PubMed
Close
,
Thian Yew Gan Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada

Search for other papers by Thian Yew Gan in
Current site
Google Scholar
PubMed
Close
, and
Xuezhi Tan Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada, and Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou, China

Search for other papers by Xuezhi Tan in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

In the past few decades, there have been more extreme climate events occurring worldwide, including Canada, which has also suffered from many extreme precipitation events. In this paper, trend analysis, probability distribution functions, principal component analysis, and wavelet analysis were used to investigate the spatial and temporal patterns of extreme precipitation events of Canada. Ten extreme precipitation indices were calculated using long-term daily precipitation data (1950–2012) from 164 Canadian gauging stations. Several large-scale climate patterns such as El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Pacific–North American (PNA), and North Atlantic Oscillation (NAO) were selected to analyze the relationships between extreme precipitation and climate indices. Convective available potential energy (CAPE), specific humidity, and surface temperature were employed to investigate potential causes of trends in extreme precipitation. The results reveal statistically significant positive trends for most extreme precipitation indices, which means that extreme precipitation of Canada has generally become more severe since the mid-twentieth century. The majority of indices display more increasing trends along the southern border of Canada while decreasing trends dominated the central Canadian Prairies. In addition, strong teleconnections are found between extreme precipitation and climate indices, but the effects of climate patterns differ from region to region. Furthermore, complex interactions of climate patterns with synoptic atmospheric circulations can also affect precipitation variability, and changes to the summer and winter extreme precipitation could be explained more by the thermodynamic impact and the combined thermodynamic and dynamic effects, respectively. The seasonal CAPE, specific humidity, and temperature are correlated to Canadian extreme precipitation, but the correlations are season dependent, which could be positive or negative.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Thian Yew Gan, tgan@ualberta.ca

Abstract

In the past few decades, there have been more extreme climate events occurring worldwide, including Canada, which has also suffered from many extreme precipitation events. In this paper, trend analysis, probability distribution functions, principal component analysis, and wavelet analysis were used to investigate the spatial and temporal patterns of extreme precipitation events of Canada. Ten extreme precipitation indices were calculated using long-term daily precipitation data (1950–2012) from 164 Canadian gauging stations. Several large-scale climate patterns such as El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Pacific–North American (PNA), and North Atlantic Oscillation (NAO) were selected to analyze the relationships between extreme precipitation and climate indices. Convective available potential energy (CAPE), specific humidity, and surface temperature were employed to investigate potential causes of trends in extreme precipitation. The results reveal statistically significant positive trends for most extreme precipitation indices, which means that extreme precipitation of Canada has generally become more severe since the mid-twentieth century. The majority of indices display more increasing trends along the southern border of Canada while decreasing trends dominated the central Canadian Prairies. In addition, strong teleconnections are found between extreme precipitation and climate indices, but the effects of climate patterns differ from region to region. Furthermore, complex interactions of climate patterns with synoptic atmospheric circulations can also affect precipitation variability, and changes to the summer and winter extreme precipitation could be explained more by the thermodynamic impact and the combined thermodynamic and dynamic effects, respectively. The seasonal CAPE, specific humidity, and temperature are correlated to Canadian extreme precipitation, but the correlations are season dependent, which could be positive or negative.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Thian Yew Gan, tgan@ualberta.ca
Save
  • Alexander, L. V., and Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, https://doi.org/10.1029/2005JD006290.

    • Search Google Scholar
    • Export Citation
  • Allan, R. P., and B. J. Soden, 2008: Atmospheric warming and the amplification of precipitation extremes. Science, 321, 14811484, https://doi.org/10.1126/science.1160787.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Asong, Z. E., M. N. Khaliq, and H. S. Wheater, 2016: Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models. Climate Dyn., 47, 29012921, https://doi.org/10.1007/s00382-016-3004-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ban, N., J. Rajczak, J. Schmidli, and C. Schär, 2018: Analysis of Alpine precipitation extremes using generalized extreme value theory in convection-resolving climate simulations. Climate Dyn., https://doi.org/10.1007/s00382-018-4339-4.

    • Search Google Scholar
    • Export Citation
  • Beniston, M., and D. B. Stephenson, 2004: Extreme climatic events and their evolution under changing climatic conditions. Global Planet. Change, 44, 19, https://doi.org/10.1016/j.gloplacha.2004.06.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamini, Y., and Y. Hochberg, 1995: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc., 57B, 289300, https://www.jstor.org/stable/2346101.

    • Search Google Scholar
    • Export Citation
  • Benyahya, L., P. Gachon, A. St-Hilaire, and R. Laprise, 2014: Frequency analysis of seasonal extreme precipitation in southern Quebec (Canada): An evaluation of regional climate model simulation with respect to two gridded datasets. Hydrol. Res., 45, 115133, https://doi.org/10.2166/nh.2013.066.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., A. R. Anderson, K. Riemann, I. Ebbers, and H. Flachs, 2007: Climatological aspects of convective parameters from the NCAR/NCEP reanalysis. Atmos. Res., 83, 294305, https://doi.org/10.1016/j.atmosres.2005.08.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, D. P., and A. C. Comrie, 2004: A winter precipitation “dipole” in the western United States associated with multidecadal ENSO variability. Geophys. Res. Lett., 31, L09203, https://doi.org/10.1029/2003GL018726.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bush, E., J. Loder, T. James, L. Mortsch, and S. Cohen, 2014: An overview of Canada’s changing climate. Canada in a changing climate: Sector perspectives on impacts and adaptation, F. J. Warren and D. S. Lemmen, Eds., Government of Canada Rep., 292 pp., https://www.nrcan.gc.ca/environment/resources/publications/impacts-adaptation/reports/assessments/2014/16309.

  • Cazelles, B., M. Chavez, D. Berteaux, F. Menard, J. O. Vik, S. Jenouvrier, and N. C. Stenseth, 2008: Wavelet analysis of ecological time series. Oecologia, 156, 287304, https://doi.org/10.1007/s00442-008-0993-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, N.-B., M. V. Vasquez, C.-F. Chen, S. Imen, and L. Mullon, 2015: Global nonlinear and nonstationary climate change effects on regional precipitation and forest phenology in Panama, Central America. Hydrol. Processes, 29, 339355, https://doi.org/10.1002/hyp.10151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, L., V. P. Singh, S. Guo, J. Zhou, J. Zhang, and P. Liu, 2015: An objective method for partitioning the entire flood season into multiple sub-seasons. J. Hydrol., 528, 621630, https://doi.org/10.1016/j.jhydrol.2015.07.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cioffi, F., U. Lall, E. Rus, and C. K. B. Krishnamurthy, 2015: Space-time structure of extreme precipitation in Europe over the last century. Int. J. Climatol., 35, 17491760, https://doi.org/10.1002/joc.4116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137, 128, https://doi.org/10.1002/qj.776.

  • Costa, A. C., and A. Soares, 2009: Trends in extreme precipitation indices derived from a daily rainfall database for the South of Portugal. Int. J. Climatol., 29, 19561975, https://doi.org/10.1002/joc.1834.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coulibaly, P., 2006: Spatial and temporal variability of Canadian seasonal precipitation (1900-2000). Adv. Water Resour., 29, 18461865, https://doi.org/10.1016/j.advwatres.2005.12.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., 2008: Temperature and pressure dependence of the rain-snow phase transition over land and ocean. Geophys. Res. Lett., 35, L12802, https://doi.org/10.1029/2008GL033295.

    • Search Google Scholar
    • Export Citation
  • Daufresne, M., K. Lengfellner, and U. Sommer, 2009. Global warming benefits the small in aquatic ecosystems. Proc. Natl. Acad. Sci. USA, 106, 12 78812 793, https://doi.org/10.1073/pnas.0902080106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dhakal, N., and B. Tharu, 2018: Spatio-temporal trends in daily precipitation extremes and their connection with North Atlantic tropical cyclones for the southeastern United States. Int. J. Climatol., 38, 38223831, https://doi.org/10.1002/joc.5535.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Lorenzo, E., and Coauthors, 2008: North Pacific Gyre Oscillation links ocean climate and ecosystem change. Geophys. Res. Lett., 35, L08607, https://doi.org/10.1029/2007GL032838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, W., Y. Lin, J. S. Wright, Y. Xie, X. Yin, and J. Guo, 2018: Precipitable water and CAPE dependence of rainfall intensities in China. Climate Dyn., https://doi.org/10.1007/s00382-018-4327-8.

    • Search Google Scholar
    • Export Citation
  • d’Orgeville, M., W. R. Peltier, A. R. Erler, and J. Gula, 2014: Climate change impacts on Great Lakes Basin precipitation extremes. J. Geophys. Res. Atmos., 119, 10 79910 812, https://doi.org/10.1002/2014JD021855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duan, W., B. He, K. Takara, P. Luo, M. Hu, N. E. Alias, and D. Nover, 2015: Changes of precipitation amounts and extremes over Japan between 1901 and 2012 and their connection to climate indices. Climate Dyn., 45, 22732292, https://doi.org/10.1007/s00382-015-2778-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Easterling, D. R., J. L. Evans, P. Ya. Groisman, T. R. Karl, K. E. Kunkel, and P. Ambenje, 2000: Observed variability and trends in extreme climate events: A brief review. Bull. Amer. Meteor. Soc., 81, 417425, https://doi.org/10.1175/1520-0477(2000)081<0417:OVATIE>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elewa, H. H., E.-S. M. Ramadan, and A. M. Nosair, 2016: Spatial-based hydro-morphometric watershed modeling for the assessment of flooding potentialities. Environ. Earth Sci., 75, 927, https://doi.org/10.1007/s12665-016-5692-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emori, S., and S. J. Brown, 2005: Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate. Geophys. Res. Lett., 32, L17706, https://doi.org/10.1029/2005GL023272.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, E. M., and R. Knutti, 2016: Observed heavy precipitation increase confirms theory and early models. Nat. Climate Change, 6, 986991, https://doi.org/10.1038/nclimate3110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frazier, A. G., and T. W. Giambelluca, 2016: Spatial trend analysis of Hawaiian rainfall from 1920 to 2012. Int. J. Climatol., 37, 25222531, https://doi.org/10.1002/joc.4862.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., C. C. Kuo, and T. Y. Gan, 2015: Change point analysis of precipitation indices of western Canada. Int. J. Climatol., 35, 25922607, https://doi.org/10.1002/joc.4144.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gan, T. Y., A. K. Gobena, and Q. Wang, 2007: Precipitation of southwestern Canada: Wavelet, scaling, multifractal analysis, and teleconnection to climate anomalies. J. Geophys. Res., 112, D10110, https://doi.org/10.1029/2006JD007157.

    • Search Google Scholar
    • Export Citation
  • Gan, T. Y., and Coauthors, 2016: Possible climate change/variability and human impacts, vulnerability of African drought prone regions, its water resources and capacity building. Hydrol. Sci. J., 61, 12091226, https://doi.org/10.1080/02626667.2015.1057143.

    • Search Google Scholar
    • Export Citation
  • Gao, L., J. Huang, X. Chen, Y. Chen, and M. Liu, 2017: Risk of extreme precipitation under nonstationarity conditions during the second flood season in the southeastern coastal region of China. J. Hydrometeor., 18, 669681, https://doi.org/10.1175/JHM-D-16-0119.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gizaw, M. S., and T. Y. Gan, 2016: Possible impact of climate change on future extreme precipitation of the Oldman, Bow and Red Deer River Basins of Alberta. Int. J. Climatol., 36, 208224, https://doi.org/10.1002/joc.4338.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gregersen, I. B., H. J. D. Sørup, H. Madsen, D. Rosbjerg, P. S. Mikkelsen, and K. Arnbjerg-Nielsen, 2013: Assessing future climatic changes of rainfall extremes at small spatio-temporal scales. Climatic Change, 118, 783797, https://doi.org/10.1007/s10584-012-0669-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gregersen, I. B., H. Madsen, D. Rosbjerg, and K. Arnbjerg-Nielsen, 2015: Long term variations of extreme rainfall in Denmark and southern Sweden. Climate Dyn., 44, 31553169, https://doi.org/10.1007/s00382-014-2276-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grinsted, A., J. C. Moore, and S. Jevrejeva, 2004: Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes Geophys., 11, 561566, https://doi.org/10.5194/npg-11-561-2004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guichard, F., and Coauthors, 2004: Modelling the diurnal cycle of deep precipitating convection over land with cloud-resolving models and single-column models. Quart. J. Roy. Meteor. Soc., 130, 31393172, https://doi.org/10.1256/qj.03.145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hales, S., S. J. Edwards, and R. S. Kovats, 2003: Impacts on health of climate extremes. Climate Change and Human Health: Risks and Responses, WMO, 79–102, http://www.who.int/globalchange/publications/climatechangechap5.pdf.

  • Hamed, K. H., and A. R. Rao, 1998: A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol., 204, 182196, https://doi.org/10.1016/S0022-1694(97)00125-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, H., J. M. Winter, E. C. Osterberg, R. M. Horton, and B. Beckage, 2017: Total and extreme precipitation changes over the northeastern United States. J. Hydrometeor., 18, 17831798, https://doi.org/10.1175/JHM-D-16-0195.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., Y. Kushnir, and M. H. Visbeck, 2001: The North Atlantic Oscillation. Science, 291, 603605, https://doi.org/10.1126/science.1058761.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., https://doi.org/10.1017/CBO9781107415324.

    • Crossref
    • Export Citation
  • IPCC, 2014: Climate Change 2014: Synthesis Report. IPCC, 151 pp., http://www.ipcc.ch/report/ar5/syr/.

  • Jiang, R., T. Y. Gan, J. Xie, and N. Wang, 2014: Spatiotemporal variability of Alberta’s seasonal precipitation, their teleconnection with large-scale climate anomalies and sea surface temperature. Int. J. Climatol., 34, 28992917, https://doi.org/10.1002/joc.3883.

    • Search Google Scholar
    • Export Citation
  • Jiang, R., T. Y. Gan, J. Xie, N. Wang, and C. C. Kuo, 2015: Historical and potential changes of precipitation and temperature of Alberta subjected to climate change impact: 1900–2100. Theor. Appl. Climatol., 127, 725739, https://doi.org/10.1007/s00704-015-1664-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, R., X. Yu, J. Xie, Y. Zhao, F. Li, and M. Yang, 2017: Recent changes in daily climate extremes in a serious water shortage metropolitan region, a case study in Jing-Jin-Ji of China. Theor. Appl. Climatol., 134, 565584, https://doi.org/10.1007/s00704-017-2293-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jolliffe, I. T., 2002: Principal Component Analysis. Springer, 488 pp.

  • Kendall, M. G., 1955: Rank Correlation Methods. C. Griffin, 196 pp.

  • Kishtawal, C. M., D. Niyogi, M. Tewari, R. A. Pielke, and J. M. Shepherd, 2010: Urbanization signature in the observed heavy rainfall climatology over India. Int. J. Climatol., 30, 19081916, https://doi.org/10.1002/joc.2044.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krichak, S. O., J. Barkan, J. S. Breitgand, S. Gualdi, and S. B. Feldstein, 2015: The role of the export of tropical moisture into midlatitudes for extreme precipitation events in the Mediterranean region. Theor. Appl. Climatol., 121, 499515, https://doi.org/10.1007/s00704-014-1244-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., 2003: North American trends in extreme precipitation. Nat. Hazards, 29, 291305, https://doi.org/10.1023/A:1023694115864.

  • Kunkel, K. E., K. Andsager, and D. D. R. Easterling, 1999: Long-term trends in extreme precipitation events over the conterminous United States and Canada. J. Climate, 12, 25152527, https://doi.org/10.1175/1520-0442(1999)012<2515:LTTIEP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., D. A. Robinson, S. Champion, X. Yin, T. Estilow, and R. M. Frankson, 2016: Trends and extremes in Northern Hemisphere snow characteristics. Curr. Climate Change Rep., 2, 6573, https://doi.org/10.1007/s40641-016-0036-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemmen, D. S., and F. J. Warren, 2004: Climate change impacts and adaptation: A Canadian perspective. Natural Resources Canada, 174 pp., https://cfs.nrcan.gc.ca/publications?id=27428.

    • Crossref
    • Export Citation
  • Lenderink, G., and E. Van Meijgaard, 2008: Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat. Geosci., 1, 511514, https://doi.org/10.1038/ngeo262.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lepore, C., D. Veneziano, and A. Molini, 2015: Temperature and CAPE dependence of rainfall extremes in the eastern United States. Geophys. Res. Lett., 42, 7483, https://doi.org/10.1002/2014GL062247.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J., Y. D. Chen, T. Y. Gan, and N.-C. Lau, 2018: Elevated increases in human-perceived temperature under climate warming. Nat. Climate Change, 8, 4347, https://doi.org/10.1038/s41558-017-0036-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Limsakul, A., and P. Singhruck, 2016: Long-term trends and variability of total and extreme precipitation in Thailand. Atmos. Res., 169, 301317, https://doi.org/10.1016/j.atmosres.2015.10.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lolis, C., and M. Türkeş, 2016: Atmospheric circulation characteristics favouring extreme precipitation in Turkey. Climate Res., 71, 139153, https://doi.org/10.3354/cr01433.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lovino, M. A., O. V. Müller, E. H. Berbery, and G. V. Müller, 2018: How have daily climate extremes changed in the recent past over northeastern Argentina? Global Planet. Change, 168, 7897, https://doi.org/10.1016/j.gloplacha.2018.06.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13, 245259, https://doi.org/10.2307/1907187.

  • Mantua, N. J., and S. R. Hare, 2002: The Pacific Decadal Oscillation. J. Oceanogr., 58, 3544, https://doi.org/10.1023/A:1015820616384.

  • Mass, C., A. Skalenakis, and M. Warner, 2011: Extreme precipitation over the west coast of North America: Is there a trend? J. Hydrometeor., 12, 310318, https://doi.org/10.1175/2010JHM1341.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mekis, É., and L. A. Vincent, 2011: An overview of the Second Generation Adjusted Daily Precipitation Dataset for trend analysis in Canada. Atmos.–Ocean, 49, 163177, https://doi.org/10.1080/07055900.2011.583910.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miao, C., H. Ashouri, K.-L. Hsu, S. Sorooshian, and Q. Duan, 2015: Evaluation of the PERSIANN-CDR daily rainfall estimates in capturing the behavior of extreme precipitation events over China. J. Hydrometeor., 16, 13871396, https://doi.org/10.1175/JHM-D-14-0174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milrad, S. M., J. R. Gyakum, and E. H. Atallah, 2015: A meteorological analysis of the 2013 Alberta flood: Antecedent large-scale flow pattern and synoptic–dynamic characteristics. Mon. Wea. Rev., 143, 28172841, https://doi.org/10.1175/MWR-D-14-00236.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mladjic, B., L. Sushama, M. N. Khaliq, R. Laprise, D. Caya, and R. Roy, 2011: Canadian RCM projected changes to extreme precipitation characteristics over Canada. J. Climate, 24, 25652584, https://doi.org/10.1175/2010JCLI3937.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monkam, D., 2002: Convective available potential energy (CAPE) in Northern Africa and tropical Atlantic and study of its connections with rainfall in Central and West Africa during summer 1985. Atmos. Res., 62, 125147, https://doi.org/10.1016/S0169-8095(02)00006-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murugavel, P., S. D. Pawar, and V. Gopalakrishnan, 2012: Trends of convective available potential energy over the Indian region and its effect on rainfall. Int. J. Climatol., 32, 13621372, https://doi.org/10.1002/joc.2359.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mwale, D., T. Y. Gan, K. Devito, C. Mendoza, U. Silins, and R. Petrone, 2009: Precipitation variability and its relationship to hydrologic variability in Alberta. Hydrol. Processes, 23, 30403056, https://doi.org/10.1002/hyp.7415.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ntale, H. K., and T. Y. Gan, 2004: East African rainfall anomaly patterns in association with El Niño/Southern Oscillation. J. Hydrol. Eng., 9, 257268, https://doi.org/10.1061/(ASCE)1084-0699(2004)9:4(257).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Gorman, P. A., 2014: Contrasting responses of mean and extreme snowfall to climate change. Nature, 512, 416418, https://doi.org/10.1038/nature13625.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Okonkwo, C., 2014: An advanced review of the relationships between Sahel precipitation and climate indices: A wavelet approach. Int. J. Atmos. Sci., 2014, 759067, https://doi.org/10.1155/2014/759067.

    • Search Google Scholar
    • Export Citation
  • Pedron, I. T., M. A. Silva Dias, S. de Paula Dias, L. M. Carvalho, and E. D. Freitas, 2017: Trends and variability in extremes of precipitation in Curitiba – Southern Brazil. Int. J. Climatol., 37, 12501264, https://doi.org/10.1002/joc.4773.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmusson, E. M., and J. M. Wallace, 1983: Meteorological aspects of the El Nino/southern oscillation. Science, 222, 11951202, https://doi.org/10.1126/science.222.4629.1195.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • R Core Team, 2017: R: A language and environment for statistical computing. R Foundation for Statistical Computing, https://www.R-project.org/.

  • Riemann-Campe, K., K. Fraedrich, and F. Lunkeit, 2009: Global climatology of convective available potential energy (CAPE) and convective inhibition (CIN) in ERA-40 reanalysis. Atmos. Res., 93, 534545, https://doi.org/10.1016/j.atmosres.2008.09.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seeley, J. T., and D. M. Romps, 2015: Why does tropical convective available potential energy (CAPE) increase with warming? Geophys. Res. Lett., 42, 10 42910 437, https://doi.org/10.1002/2015GL066199.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s Tau. J. Amer. Stat. Assoc., 63, 13791389, https://doi.org/10.1080/01621459.1968.10480934.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shadmani, M., S. Marofi, and M. Roknian, 2012: Trend analysis in reference evapotranspiration using Mann-Kendall and Spearman’s Rho tests in arid regions of Iran. Water Resour. Manage., 26, 211224, https://doi.org/10.1007/s11269-011-9913-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shephard, M. W., E. Mekis, R. J. Morris, Y. Feng, X. Zhang, K. Kilcup, and R. Fleetwood, 2014: Trends in Canadian short-duration extreme rainfall: Including an intensity-duration-frequency perspective. Atmos.–Ocean, 52, 398417, https://doi.org/10.1080/07055900.2014.969677.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simonovic, S. P., A. Schardong, and D. Sandink, 2016: Mapping extreme rainfall statistics for Canada under climate change using updated intensity-duration-frequency curves. J. Water Resour. Plan. Manage., 143, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000725.

    • Search Google Scholar
    • Export Citation
  • Song, X., S. Song, W. Sun, X. Mu, S. Wang, J. Li, and Y. Li, 2015: Recent changes in extreme precipitation and drought over the Songhua River Basin, China, during 1960–2013. Atmos. Res., 157, 137152, https://doi.org/10.1016/j.atmosres.2015.01.022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spearman, C., 1904: The proof and measurement of association between two things. Amer. J. Psychol., 15, 72101, https://doi.org/10.2307/1412159.

  • Tan, X., and T. Y. Gan, 2017: Non-stationary analysis of the frequency and intensity of heavy precipitation over Canada and their relations to large-scale climate patterns. Climate Dyn., 48, 29833001, https://doi.org/10.1007/s00382-016-3246-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, X., T. Y. Gan, S. Chen, and B. Liu, 2018a: Modeling distributional changes in winter precipitation of Canada using Bayesian spatiotemporal quantile regression subjected to different teleconnections. Climate Dyn., https://doi.org/10.1007/s00382-018-4241-0.

    • Search Google Scholar
    • Export Citation
  • Tan, X., T. Y. Gan, and Y. D. Chen, 2018b: Moisture sources and pathways associated with the spatial variability of seasonal extreme precipitation over Canada. Climate Dyn., 50, 629640, https://doi.org/10.1007/s00382-017-3630-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, X., T. Y. Gan, and Y. D. Chen, 2018c: Synoptic moisture pathways associated with mean and extreme precipitation over Canada for summer and fall. Climate Dyn., https://doi.org/10.1007/s00382-018-4300-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tandon, N. F., X. Zhang, and A. H. Sobel, 2018: Understanding the dynamics of future changes in extreme precipitation intensity. Geophys. Res. Lett., 45, 28702878, https://doi.org/10.1002/2017GL076361.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Theobald, A., H. Mcgowan, and J. Speirs, 2018: Teleconnection influence of precipitation-bearing synoptic types over the Snowy Mountains region of south-east Australia. Int. J. Climatol., 38, 27432759, https://doi.org/10.1002/joc.5457.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet analysis. Bull. Amer. Meteor. Soc., 79, 6178, https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ventura, V., C. J. Paciorek, and J. S. Risbey, 2004: Controlling the proportion of falsely rejected hypotheses when conducting multiple tests with climatological data. J. Climate, 17, 43434356, https://doi.org/10.1175/3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vincent, L. A., and É. Mekis, 2006: Changes in daily and extreme temperature and precipitation indices for Canada over the twentieth century. Atmos.–Ocean, 44, 177193, https://doi.org/10.3137/ao.440205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vincent, L. A., X. Zhang, R. D. Brown, Y. Feng, E. Mekis, E. J. Milewska, H. Wan, and X. L. Wang, 2015: Observed trends in Canada’s climate and influence of low-frequency variability modes. J. Climate, 28, 45454560, https://doi.org/10.1175/JCLI-D-14-00697.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., M. Zhang, J. Wei, S. Wang, S. Li, Q. Ma, X. Li, and S. Pan, 2013: Changes in extreme events of temperature and precipitation over Xinjiang, northwest China, during 1960–2009. Quat. Int., 298, 141151, https://doi.org/10.1016/j.quaint.2012.09.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., and X. Zhang, 2008: Downscaling and projection of winter extreme daily precipitation over North America. J. Climate, 21, 923937, https://doi.org/10.1175/2007JCLI1671.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., G. Huang, and J. Liu, 2014: Projected increases in intensity and frequency of rainfall extremes through a regional climate modeling approach. J. Geophys. Res. Atmos., 119, 13 27113 286, https://doi.org/10.1002/2014JD022564.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., E. A. McBean, and P. Jarrett, 2015: Identification of changes in heavy rainfall events in Ontario, Canada. Stochastic Environ. Res. Risk Assess., 29, 19491962, https://doi.org/10.1007/s00477-015-1085-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wolter, K., and M. S. Timlin, 1993: Monitoring ENSO in COADS with a seasonally adjusted principal component index. Proc. 17th Climate Diagnostics Workshop, Norman, OK, NOAA, 52–57.

  • Wolter, K., and M. S. Timlin, 1998: Measuring the strength of ENSO events: How does 1997/98 rank? Weather, 53, 315324, https://doi.org/10.1002/j.1477-8696.1998.tb06408.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wolter, K., and M. S. Timlin, 2011: El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext). Int. J. Climatol., 31, 10741087, https://doi.org/10.1002/joc.2336.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xi, Y., C. Miao, J. Wu