Editorial Type:
Article
Article Type:
Research Article

Changes of Extreme Precipitation in CMIP6 Projections: Should We Use Stationary or Nonstationary Models?

Hebatallah Mohamed Abdelmoaty aDepartment of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
bIrrigation and Hydraulics Department, Faculty of Engineering, Cairo University, Giza, Egypt

Search for other papers by Hebatallah Mohamed Abdelmoaty in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0002-2288-691X
and
Simon Michael Papalexiou aDepartment of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
cGlobal Institute for Water Security, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
dFaculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic

Search for other papers by Simon Michael Papalexiou in
Current site
Google Scholar
PubMed Close
Online Publication:
31 Mar 2023
Print Publication:
01 May 2023
DOI:
https://doi.org/10.1175/JCLI-D-22-0467.1
Page(s):
2999–3014
Restricted access

Abstract

With global warming, the behavior of extreme precipitation shifts toward nonstationarity. Here, we analyze the annual maxima of daily precipitation (AMP) all over the globe using projections of the latest phase of the Coupled Model Intercomparison Project (CMIP6) under four shared socioeconomic pathways (SSPs). The projections were bias corrected using a semiparametric quantile mapping, a novel technique extended to extreme precipitation. This analysis 1) explores the variability of future AMP globally and 2) investigates the performance of stationary and nonstationary models in describing future AMP with trends. The results show that global warming potentially intensifies AMP. For the nonparametric analysis, the 33-yr precipitation levels are increasing up to 33.2 mm compared to the historical period. The parametric analysis shows that the return period of 100-yr historical events will decrease approximately to 50 and 70 years in the Northern and Southern Hemispheres, respectively. Under the highest emission scenario, the projected 100-yr levels are expected to increase by 7.5%–21% over the historical levels. Using stationary models to estimate the 100-yr return level for AMP projections with trends leads to an underestimation of 3.4% on average. Extensive Monte Carlo experiments are implemented to explain this underestimation.

© 2023 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: Hebatallah M. Abdelmoaty, heba.abdelmoaty@ucalgary.ca

Abstract

With global warming, the behavior of extreme precipitation shifts toward nonstationarity. Here, we analyze the annual maxima of daily precipitation (AMP) all over the globe using projections of the latest phase of the Coupled Model Intercomparison Project (CMIP6) under four shared socioeconomic pathways (SSPs). The projections were bias corrected using a semiparametric quantile mapping, a novel technique extended to extreme precipitation. This analysis 1) explores the variability of future AMP globally and 2) investigates the performance of stationary and nonstationary models in describing future AMP with trends. The results show that global warming potentially intensifies AMP. For the nonparametric analysis, the 33-yr precipitation levels are increasing up to 33.2 mm compared to the historical period. The parametric analysis shows that the return period of 100-yr historical events will decrease approximately to 50 and 70 years in the Northern and Southern Hemispheres, respectively. Under the highest emission scenario, the projected 100-yr levels are expected to increase by 7.5%–21% over the historical levels. Using stationary models to estimate the 100-yr return level for AMP projections with trends leads to an underestimation of 3.4% on average. Extensive Monte Carlo experiments are implemented to explain this underestimation.

© 2023 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: Hebatallah M. Abdelmoaty, heba.abdelmoaty@ucalgary.ca

Supplementary Materials

    • Supplemental Materials (PDF 2.02 MB)
Save
Cover Journal of Climate
Journal of Climate

  • AghaKouchak, A., and Coauthors, 2020: Climate extremes and compound hazards in a warming world. Annu. Rev. Earth Planet. Sci., 48, 519–548, https://doi.org/10.1146/annurev-earth-071719-055228.

    • Search Google Scholar
    • Export Citation

  • Ahmadalipour, A., A. Rana, H. Moradkhani, and A. Sharma, 2017: Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis. Theor. Appl. Climatol., 128, 71–87, https://doi.org/10.1007/s00704-015-1695-4.

    • Search Google Scholar
    • Export Citation

  • Akaike, H., 1974: A new look at the statistical model identification. IEEE Trans. Autom. Control, 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705.

    • Search Google Scholar
    • Export Citation

  • Anderson, D. R., and K. P. Burnham, 2002: Avoiding pitfalls when using information-theoretic methods. J. Wildl. Manage., 66, 912–918, https://doi.org/10.2307/3803155.

    • Search Google Scholar
    • Export Citation

  • Anderson, T. W., and D. A. Darling, 1954: A test of goodness of fit. J. Amer. Stat. Assoc., 49, 765–769, https://doi.org/10.1080/01621459.1954.10501232.

    • Search Google Scholar
    • Export Citation

  • Ayugi, B., V. Dike, H. Ngoma, H. Babaousmail, and V. Ongoma, 2021: Future changes in precipitation extremes over East Africa based on CMIP6 models. Water, 13, 2358, https://doi.org/10.3390/w13172358.

    • Search Google Scholar
    • Export Citation

  • Becker, A., P. Finger, A. Meyer-Christo, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese, 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present. Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013.

    • Search Google Scholar
    • Export Citation

  • Bellprat, O., and Coauthors, 2015: Unusual past dry and wet rainy seasons over southern Africa and South America from a climate perspective. Wea. Climate Extremes, 9, 36–46, https://doi.org/10.1016/j.wace.2015.07.001.

    • 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, 181–185, https://doi.org/10.1038/ngeo1731.

    • Search Google Scholar
    • Export Citation

  • Bony, S., and J.-L. Dufresne, 2005: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett., 32, L20806, https://doi.org/10.1029/2005GL023851.

    • Search Google Scholar
    • Export Citation

  • Boomsma, A., 1985: Nonconvergence, improper solutions, and starting values in LISREL maximum likelihood estimation. Psychometrika, 50, 229–242, https://doi.org/10.1007/BF02294248.

    • Search Google Scholar
    • Export Citation

  • Cabré, M. F., S. Solman, and M. Núñez, 2016: Regional climate change scenarios over southern South America for future climate (2080–2099) using the MM5 model. Mean, interannual variability and uncertainties. Atmósfera, 29, 35–60, https://doi.org/10.20937/ATM.2016.29.01.04.

    • Search Google Scholar
    • Export Citation

  • Cannon, A. J., 2018: Multivariate quantile mapping bias correction: An N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dyn., 50, 31–49, https://doi.org/10.1007/s00382-017-3580-6.

    • Search Google Scholar
    • Export Citation

  • Cannon, A. J., S. R. Sobie, and T. Q. Murdock, 2015: Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? J. Climate, 28, 6938–6959, https://doi.org/10.1175/JCLI-D-14-00754.1.

    • Search Google Scholar
    • Export Citation

  • Collins, M., and Coauthors, 2010: The impact of global warming on the tropical Pacific Ocean and El Niño. Nat. Geosci., 3, 391–397, https://doi.org/10.1038/ngeo868.

    • Search Google Scholar
    • Export Citation

  • Contractor, S., and Coauthors, 2020: Rainfall estimates on a gridded network (REGEN)—A global land-based gridded dataset of daily precipitation from 1950 to 2016. Hydrol. Earth Syst. Sci., 24, 919–943, https://doi.org/10.5194/hess-24-919-2020.

    • Search Google Scholar
    • Export Citation

  • Cooley, D., 2013: Return periods and return levels under climate change. Extremes in a Changing Climate, A. AghaKouchak et al., Eds., Water Science and Technology Library, Vol. 65, Springer, 97–114, https://doi.org/10.1007/978-94-007-4479-0_4.

  • De Paola, F., M. Giugni, F. Pugliese, A. Annis, and F. Nardi, 2018: GEV parameter estimation and stationary vs. non-stationary analysis of extreme rainfall in African test cities. Hydrology, 5, 28, https://doi.org/10.3390/hydrology5020028.

    • Search Google Scholar
    • Export Citation

  • Eden, J. M., M. Widmann, D. Grawe, and S. Rast, 2012: Skill, correction, and downscaling of GCM-simulated precipitation. J. Climate, 25, 3970–3984, https://doi.org/10.1175/JCLI-D-11-00254.1.

    • Search Google Scholar
    • Export Citation

  • Eden, J. M., K. Wolter, F. E. L. Otto, and G. Jan van Oldenborgh, 2016: Multi-method attribution analysis of extreme precipitation in Boulder, Colorado. Environ. Res. Lett., 11, 124009, https://doi.org/10.1088/1748-9326/11/12/124009.

    • Search Google Scholar
    • Export Citation

  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Search Google Scholar
    • Export Citation

  • Gao, M., D. Mo, and X. Wu, 2016: Nonstationary modeling of extreme precipitation in China. Atmos. Res., 182, 1–9, https://doi.org/10.1016/j.atmosres.2016.07.014.

    • Search Google Scholar
    • Export Citation

  • Ge, F., S. Zhu, H. Luo, X. Zhi, and H. Wang, 2021: Future changes in precipitation extremes over Southeast Asia: Insights from CMIP6 multi-model ensemble. Environ. Res. Lett., 16, 024013, https://doi.org/10.1088/1748-9326/abd7ad.

    • Search Google Scholar
    • Export Citation

  • Hobijn, B., P. H. Franses, and M. Ooms, 2004: Generalizations of the KPSS-test for stationarity. Stat. Neerl., 58, 483–502, https://doi.org/10.1111/j.1467-9574.2004.00272.x.

    • Search Google Scholar
    • Export Citation

  • Jones, P. W., 1999: First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon. Wea. Rev., 127, 2204–2210, https://doi.org/10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kendall, M. G., 1948: Rank Correlation Methods. 3rd ed. Griffin, 160 pp.

  • Kharin, V. V., F. W. Zwiers, X. Zhang, and M. Wehner, 2013: Changes in temperature and precipitation extremes in the CMIP5 ensemble. Climatic Change, 119, 345–357, https://doi.org/10.1007/s10584-013-0705-8.

    • Search Google Scholar
    • Export Citation

  • Kirchmeier-Young, M. C., and X. Zhang, 2020: Human influence has intensified extreme precipitation in North America. Proc. Natl. Acad. Sci. USA, 117, 13 308–13 313, https://doi.org/10.1073/pnas.1921628117.

    • Search Google Scholar
    • Export Citation

  • Kwiatkowski, D., P. C. B. Phillips, P. Schmidt, and Y. Shin, 1992: Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? J. Econometrics, 54, 159–178, https://doi.org/10.1016/0304-4076(92)90104-Y.

    • Search Google Scholar
    • Export Citation

  • Lee, Y., Y. Shin, K. Boo, and J. Park, 2020: Future projections and uncertainty assessment of precipitation extremes in the Korean Peninsula from the CMIP5 ensemble. Atmos. Sci. Lett., 21, e954, https://doi.org/10.1002/asl.954.

    • Search Google Scholar
    • Export Citation

  • Lenderink, G., and E. van Meijgaard, 2009: Unexpected rise in extreme precipitation caused by a shift in rain type? Nat. Geosci., 2, 373, https://doi.org/10.1038/ngeo524.

    • Search Google Scholar
    • Export Citation

  • Lenderink, G., and J. Attema, 2015: A simple scaling approach to produce climate scenarios of local precipitation extremes for the Netherlands. Environ. Res. Lett., 10, 085001, https://doi.org/10.1088/1748-9326/10/8/085001.

    • Search Google Scholar
    • Export Citation

  • Li, H., J. Sheffield, and E. F. Wood, 2010: Bias correction of monthly precipitation and temperature fields from intergovernmental panel on climate change AR4 models using equidistant quantile matching. J. Geophys. Res., 115, D10101, https://doi.org/10.1029/2009JD012882.

    • Search Google Scholar
    • Export Citation

  • Libanda, B., and C. Ngonga, 2018: Projection of frequency and intensity of extreme precipitation in Zambia: A CMIP5 study. Climate Res., 76, 59–72, https://doi.org/10.3354/cr01528.

    • Search Google Scholar
    • Export Citation

  • Liu, J., D. Shangguan, S. Liu, Y. Ding, S. Wang, and X. Wang, 2019: Evaluation and comparison of CHIRPS and MSWEP daily-precipitation products in the Qinghai-Tibet Plateau during the period of 1981–2015. Atmos. Res., 230, 104634, https://doi.org/10.1016/j.atmosres.2019.104634.

    • Search Google Scholar
    • Export Citation

  • Luceño, A., 2006: Fitting the generalized Pareto distribution to data using maximum goodness-of-fit estimators. Comput. Stat. Data Anal., 51, 904–917, https://doi.org/10.1016/j.csda.2005.09.011.

    • Search Google Scholar
    • Export Citation

  • Lüdecke, H.-J., G. Müller-Plath, M. G. Wallace, and S. Lüning, 2021: Decadal and multidecadal natural variability of African rainfall. J. Hydrol., 34, 100795, https://doi.org/10.1016/j.ejrh.2021.100795.

    • Search Google Scholar
    • Export Citation

  • Lyon, B., and N. Vigaud, 2017: Unraveling East Africa’s climate paradox. Climate Extremes: Patterns and Mechanisms, John Wiley and Sons, 265–281.

  • Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13, 245–259, https://doi.org/10.2307/1907187.

  • Maraun, D., 2013: Bias correction, quantile mapping, and downscaling: Revisiting the inflation issue. J. Climate, 26, 2137–2143, https://doi.org/10.1175/JCLI-D-12-00821.1.

    • Search Google Scholar
    • Export Citation

  • Min, S.-K., X. Zhang, F. W. Zwiers, and G. C. Hegerl, 2011: Human contribution to more-intense precipitation extremes. Nature, 470, 378–381, https://doi.org/10.1038/nature09763.

    • Search Google Scholar
    • Export Citation

  • Monjo, R., E. Gaitán, J. Pórtoles, J. Ribalaygua, and L. Torres, 2016: Changes in extreme precipitation over Spain using statistical downscaling of CMIP5 projections. Int. J. Climatol., 36, 757–769, https://doi.org/10.1002/joc.4380.

    • Search Google Scholar
    • Export Citation

  • Nerantzaki, S. D., and S. M. Papalexiou, 2021: Assessing extremes in hydroclimatology: A review on probabilistic methods. J. Hydrol., 605, 127302, https://doi.org/10.1016/j.jhydrol.2021.127302.

    • Search Google Scholar
    • Export Citation

  • Ngoma, H., W. Wen, B. Ayugi, H. Babaousmail, R. Karim, and V. Ongoma, 2021: Evaluation of precipitation simulations in CMIP6 models over Uganda. Int. J. Climatol., 41, 4743–4768, https://doi.org/10.1002/joc.7098.

    • Search Google Scholar
    • Export Citation

  • O’Neill, B. C., and Coauthors, 2016: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016.

    • Search Google Scholar
    • Export Citation

  • Ouarda, T. B. M. J., and C. Charron, 2019: Changes in the distribution of hydro-climatic extremes in a nonstationary framework. Sci. Rep., 9, 8104, https://doi.org/10.1038/s41598-019-44603-7.

    • Search Google Scholar
    • Export Citation

  • Pall, P., M. R. Allen, and D. A. Stone, 2007: Testing the Clausius–Clapeyron constraint on changes in extreme precipitation under CO2 warming. Climate Dyn., 28, 351–363, https://doi.org/10.1007/s00382-006-0180-2.

    • Search Google Scholar
    • Export Citation

  • Panagoulia, D., P. Economou, and C. Caroni, 2014: Stationary and nonstationary generalized extreme value modelling of extreme precipitation over a mountainous area under climate change. Environmetrics, 25, 29–43, https://doi.org/10.1002/env.2252.

    • Search Google Scholar
    • Export Citation

  • Papalexiou, S. M., and D. Koutsoyiannis, 2013: Battle of extreme value distributions: A global survey on extreme daily rainfall. Water Resour. Res., 49, 187–201, https://doi.org/10.1029/2012WR012557.

    • Search Google Scholar
    • Export Citation

  • Philip, S., and Coauthors, 2020: A protocol for probabilistic extreme event attribution analyses. Adv. Stat. Climatol. Meteor. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020.

    • Search Google Scholar
    • Export Citation

  • Piani, C., G. P. Weedon, M. Best, S. M. Gomes, P. Viterbo, S. Hagemann, and J. O. Haerter, 2010: Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J. Hydrol., 395, 199–215, https://doi.org/10.1016/j.jhydrol.2010.10.024.

    • Search Google Scholar
    • Export Citation

  • Prospero, J. M., F.-X. Collard, J. Molinié, and A. Jeannot, 2014: Characterizing the annual cycle of African dust transport to the Caribbean Basin and South America and its impact on the environment and air quality Global Biogeochem. Cycles, 28, 757–773, https://doi.org/10.1002/2013GB004802.

    • Search Google Scholar
    • Export Citation

  • Rajulapati, C. R., H. M. Abdelmoaty, S. D. Nerantzaki, and S. M. Papalexiou, 2022a: Changes in the risk of extreme temperatures in megacities worldwide. Climate Risk Manage., 36, 100433, https://doi.org/10.1016/j.crm.2022.100433.

    • Search Google Scholar
    • Export Citation

  • Rajulapati, C. R., R. K. Gaddam, S. D. Nerantzaki, S. M. Papalexiou, A. J. Cannon, and M. P. Clark, 2022b: Exacerbated heat in large Canadian cities. Urban Climate, 42, 101097, https://doi.org/10.1016/j.uclim.2022.101097.

    • Search Google Scholar
    • Export Citation

  • Read, L. K., and R. M. Vogel, 2015: Reliability, return periods, and risk under nonstationarity. Water Resour. Res., 51, 6381–6398, https://doi.org/10.1002/2015WR017089.

    • Search Google Scholar
    • Export Citation

  • Risser, M. D., and M. F. Wehner, 2017: Attributable human‐induced changes in the likelihood and magnitude of the observed extreme precipitation during Hurricane Harvey. Geophys. Res. Lett., 44, 12 457–12 464, https://doi.org/10.1002/2017GL075888.

    • Search Google Scholar
    • Export Citation

  • Salas, J. D., and J. Obeysekera, 2014: Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. J. Hydrol. Eng., 19, 554–568, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000820.

    • Search Google Scholar
    • Export Citation

  • Schneider, U., P. Finger, A. Meyer-Christoffer, E. Rustemeier, M. Ziese, and A. Becker, 2017: Evaluating the hydrological cycle over land using the newly-corrected precipitation climatology from the Global Precipitation Climatology Centre (GPCC). Atmosphere, 8, 52, https://doi.org/10.3390/atmos8030052.

    • Search Google Scholar
    • Export Citation

  • Schwarz, G., 1978: Estimating the dimension of a model. Ann. Stat., 6, 461–464, https://doi.org/10.1214/aos/1176344136.

  • Scoccimarro, E., and S. Gualdi, 2020: Heavy daily precipitation events in the CMIP6 worst-case scenario: Projected twenty-first-century changes. J. Climate, 33, 7631–7642, https://doi.org/10.1175/JCLI-D-19-0940.1.

    • Search Google Scholar
    • Export Citation

  • Scoccimarro, E., S. Gualdi, A. Bellucci, M. Zampieri, and A. Navarra, 2013: Heavy precipitation events in a warmer climate: Results from CMIP5 models. J. Climate, 26, 7902–7911, https://doi.org/10.1175/JCLI-D-12-00850.1.

    • Search Google Scholar
    • Export Citation

  • Serago, J. M., and R. M. Vogel, 2018: Parsimonious nonstationary flood frequency analysis. Adv. Water Resour., 112, 1–16, https://doi.org/10.1016/j.advwatres.2017.11.026.

    • Search Google Scholar
    • Export Citation

  • Serinaldi, F., and C. G. Kilsby, 2015: Stationarity is undead: Uncertainty dominates the distribution of extremes. Adv. Water Resour., 77, 17–36, https://doi.org/10.1016/j.advwatres.2014.12.013.

    • Search Google Scholar
    • Export Citation

  • Sillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaugh, 2013: Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J. Geophys. Res. Atmos., 118, 2473–2493, https://doi.org/10.1002/jgrd.50188.

    • Search Google Scholar
    • Export Citation

  • Singh, N., and P. Chinnasamy, 2021: Non-stationary flood frequency analysis and attribution of streamflow series: A case study of Periyar River, India. Hydrol. Sci. J., 66, 1866–1881, https://doi.org/10.1080/02626667.2021.1968406.

    • Search Google Scholar
    • Export Citation

  • Šraj, M., A. Viglione, J. Parajka, and G. Blöschl, 2016: The influence of non-stationarity in extreme hydrological events on flood frequency estimation. J. Hydrol. Hydromech., 64, 426–437, https://doi.org/10.1515/johh-2016-0032.

    • Search Google Scholar
    • Export Citation

  • Stouffer, R. J., V. Eyring, G. A. Meehl, S. Bony, C. Senior, B. Stevens, and K. E. Taylor, 2017: CMIP5 scientific gaps and recommendations for CMIP6. Bull. Amer. Meteor. Soc., 98, 95–105, https://doi.org/10.1175/BAMS-D-15-00013.1.

    • Search Google Scholar
    • Export Citation

  • Thibeault, J. M., and A. Seth, 2014: Changing climate extremes in the northeast United States: Observations and projections from CMIP5. Climatic Change, 127, 273–287, https://doi.org/10.1007/s10584-014-1257-2.

    • Search Google Scholar
    • Export Citation

  • Tian, Q., Z. Li, and X. Sun, 2021: Frequency analysis of precipitation extremes under a changing climate: A case study in Heihe River basin, China. J. Water Climate Change, 12, 772–786, https://doi.org/10.2166/wcc.2020.170.

    • Search Google Scholar
    • Export Citation

  • Um, M.-J., Y. Kim, M. Markus, and D. J. Wuebbles, 2017: Modeling nonstationary extreme value distributions with nonlinear functions: An application using multiple precipitation projections for U.S. cities. J. Hydrol., 552, 396–406, https://doi.org/10.1016/j.jhydrol.2017.07.007.

    • Search Google Scholar
    • Export Citation

  • Utsumi, N., S. Seto, S. Kanae, E. E. Maeda, and T. Oki, 2011: Does higher surface temperature intensify extreme precipitation? Geophys. Res. Lett., 38, L16708, https://doi.org/10.1029/2011GL048426.

    • Search Google Scholar
    • Export Citation

  • van Oldenborgh, G. J., and Coauthors, 2017: Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environ. Res. Lett., 12, 124009, https://doi.org/10.1088/1748-9326/aa9ef2.

    • Search Google Scholar
    • Export Citation

  • Vasiliades, L., P. Galiatsatou, and A. Loukas, 2015: Nonstationary frequency analysis of annual maximum rainfall using climate covariates. Water Resour. Manage., 29, 339–358, https://doi.org/10.1007/s11269-014-0761-5.

    • Search Google Scholar
    • Export Citation

  • von Schuckmann, K., and Coauthors, 2020: Heat stored in the Earth system: Where does the energy go? Earth Syst. Sci. Data, 12, 2013–2041, https://doi.org/10.5194/essd-12-2013-2020.

    • Search Google Scholar
    • Export Citation

  • Wang, S., J. Huang, Y. He, and Y. Guan, 2014: Combined effects of the Pacific decadal oscillation and El Niño-Southern Oscillation on global land dry–wet changes. Sci. Rep., 4, 6651, https://doi.org/10.1038/srep06651.

    • Search Google Scholar
    • Export Citation

  • Wehner, M., P. Gleckler, and J. Lee, 2020: Characterization of long period return values of extreme daily temperature and precipitation in the CMIP6 models: Part 1, model evaluation. Wea. Climate Extremes, 30, 100283, https://doi.org/10.1016/j.wace.2020.100283.

    • Search Google Scholar
    • Export Citation

  • Wengel, C., S.-S. Lee, M. F. Stuecker, A. Timmermann, J.-E. Chu, and F. Schloesser, 2021: Future high-resolution El Niño/Southern Oscillation dynamics. Nat. Climate Change, 11, 758–765, https://doi.org/10.1038/s41558-021-01132-4.

    • Search Google Scholar
    • Export Citation

  • Wi, S., J. B. Valdés, S. Steinschneider, and T.-W. Kim, 2016: Nonstationary frequency analysis of extreme precipitation in South Korea using peaks-over-threshold and annual maxima. Stochastic Environ. Res. Risk Assess., 30, 583–606, https://doi.org/10.1007/s00477-015-1180-8.

    • Search Google Scholar
    • Export Citation

  • Wuebbles, D., and Coauthors, 2014: CMIP5 climate model analyses: Climate extremes in the United States. Bull. Amer. Meteor. Soc., 95, 571–583, https://doi.org/10.1175/BAMS-D-12-00172.1.

    • Search Google Scholar
    • Export Citation

  • Xavier, A. C. F., G. C. Blain, M. V. B. de Morais, and G. da R. Sobierajski, 2019: Selecting “the best” nonstationary generalized extreme value (GEV) distribution: On the influence of different numbers of GEV-models. Bragantia, 78, 606–621, https://doi.org/10.1590/1678-4499.20180408.

    • Search Google Scholar
    • Export Citation

  • Xu, Y., X. Gao, F. Giorgi, B. Zhou, Y. Shi, J. Wu, and Y. Zhang, 2018: Projected changes in temperature and precipitation extremes over China as measured by 50-yr return values and periods based on a CMIP5 ensemble. Adv. Atmos. Sci., 35, 376–388, https://doi.org/10.1007/s00376-017-6269-1.

    • Search Google Scholar
    • Export Citation

  • Yang, L., J. Li, H. Sun, Y. Guo, and B. A. Engel, 2019: Calculation of nonstationary flood return period considering historical extraordinary flood events. J. Flood Risk Manage., 12, e12463, https://doi.org/10.1111/jfr3.12463.

    • Search Google Scholar
    • Export Citation

  • Zarrin, A., and A. Dadashi-Roudbari, 2021: Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theor. Appl. Climatol., 144, 643–660, https://doi.org/10.1007/s00704-021-03568-2.

    • Search Google Scholar
    • Export Citation

  • Zhou, B., Q. H. Wen, Y. Xu, L. Song, and X. Zhang, 2014: Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles. J. Climate, 27, 6591–6611, https://doi.org/10.1175/JCLI-D-13-00761.1.

    • Search Google Scholar
    • Export Citation

  • Zhu, X., and Coauthors, 2021: Extreme climate changes over three major river basins in China as seen in CMIP5 and CMIP6. Climate Dyn., 57, 1187–1205, https://doi.org/10.1007/s00382-021-05767-z.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 1185 1185 43
Full Text Views 373 373 16
PDF Downloads 536 536 17