• Abatzoglou, J. T., 2013: Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33, 121131, https://doi.org/10.1002/joc.3413.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Abatzoglou, J. T., and T. J. Brown, 2012: A comparison of statistical downscaling methods suited for wildfire applications. Int. J. Climatol., 32, 772780, https://doi.org/10.1002/joc.2312.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Agel, L., M. Barlow, J. Polonia, and D. Coe, 2020: Simulation of northeast U.S. extreme precipitation and its associated circulation by CMIP5 models. J. Climate, 33, 98179834, https://doi.org/10.1175/JCLI-D-19-0757.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • AghaKouchak, A., D. Easterling, K. Hsu, S. Schubert, and S. Sorooshian, Eds., 2012: Extremes in a Changing Climate: Detection, Analysis and Uncertainty. Water Science and Technology Library, Vol. 65, Springer, 426 pp.

    • Search Google Scholar
    • Export Citation
  • Alduchov, O. A., and R. E. Eskridge, 1996: Improved Magnus form approximation of saturation vapor pressure. J. Appl. Meteor. Climatol., 35, 601609, https://doi.org/10.1175/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ali, H., and V. Mishra, 2018: Contributions of dynamic and thermodynamic scaling in subdaily precipitation extremes in India. Geophys. Res. Lett., 45, 23522361, https://doi.org/10.1002/2018GL077065.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ali, H., H. J. Fowler, and V. Mishra, 2018: Global observational evidence of strong linkage between dew point temperature and precipitation extremes. Geophys. Res. Lett., 45, 12 32012 330, https://doi.org/10.1029/2018GL080557.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ali, H., N. Peleg, and H. J. Fowler, 2021: Global scaling of rainfall with dewpoint temperature reveals considerable ocean‐land difference. Geophys. Res. Lett., 48, e2021GL093798, https://doi.org/10.1029/2021GL093798.

    • Crossref
    • 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
  • Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419, 228232, https://doi.org/10.1038/nature01092.

    • Search Google Scholar
    • Export Citation
  • Attema, J. J., J. M. Loriaux, and G. Lenderink, 2014: Extreme precipitation response to climate perturbations in an atmospheric mesoscale model. Environ. Res. Lett., 9, 014003, https://doi.org/10.1088/1748-9326/9/1/014003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ban, N., J. Schmidli, and C. Schär, 2015: Heavy precipitation in a changing climate: Does short‐term summer precipitation increase faster? Geophys. Res. Lett., 42, 11651172, https://doi.org/10.1002/2014GL062588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, E. A., and L. Polvani, 2013: Response of the midlatitude jets, and of their variability, to increased greenhouse gases in the CMIP5 models. J. Climate, 26, 71177135, https://doi.org/10.1175/JCLI-D-12-00536.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bello, A. P., A. Mailhot, and D. Paquin, 2021: The response of daily and sub‐daily extreme precipitations to changes in surface and dew point temperatures. J. Geophys. Res. Atmos., 126, e2021JD034972, https://doi.org/10.1029/2021JD034972.

    • Search Google Scholar
    • Export Citation
  • Berg, P., J. O. Haerter, P. Thejll, C. Piani, S. Hagemann, and J. H. Christensen, 2009: Seasonal characteristics of the relationship between daily precipitation intensity and surface temperature. J. Geophys. Res., 114, D18102, https://doi.org/10.1029/2009JD012008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blenkinsop, S., S. C. Chan, E. J. Kendon, N. M. Roberts, and H. J. Fowler, 2015: Temperature influences on intense UK hourly precipitation and dependency on large-scale circulation. Environ. Res. Lett., 10, 054021, https://doi.org/10.1088/1748-9326/10/5/054021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coles, S., J. Bawa, L. Trenner, and P. Dorazio, 2001: An Introduction to Statistical Modeling of Extreme Values. Springer, 209 pp.

  • Cubasch, U., and Coauthors, 2001: Projections of future climate change. Climate Change 2001: The Scientific Basis, Y. Ding et al., Eds., Cambridge University Press, 526582.

    • Search Google Scholar
    • Export Citation
  • DeGaetano, A. T., and C. M. Castellano, 2017: Future projections of extreme precipitation intensity-duration-frequency curves for climate adaptation planning in New York State. Climate Serv., 5, 2335, https://doi.org/10.1016/j.cliser.2017.03.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., A. Phillips, V. Bourdette, and H. Teng, 2012: Uncertainty in climate change projections: the role of internal variability. Climate Dyn., 38, 527546, https://doi.org/10.1007/s00382-010-0977-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drobinski, P., and Coauthors, 2018: Scaling precipitation extremes with temperature in the Mediterranean: Past climate assessment and projection in anthropogenic scenarios. Climate Dyn., 51, 12371257, https://doi.org/10.1007/s00382-016-3083-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duane, S., A. D. Kennedy, B. J. Pendleton, and D. Roweth, 1987: Hybrid Monte Carlo. Phys. Lett. B, 195, 216222, https://doi.org/10.1016/0370-2693(87)91197-X.

    • 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
  • Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741866.

    • Search Google Scholar
    • Export Citation
  • Fosser, G., E. J. Kendon, D. Stephenson, and S. Tucker, 2020: Convection-permitting models offer promise of more certain extreme rainfall projections. Geophys. Res. Lett., 47, e2020GL088151, https://doi.org/10.1029/2020GL088151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fowler, H. J., S. Blenkinsop, and C. Tebaldi, 2007: Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modelling. Int. J. Climatol., 27, 15471578, https://doi.org/10.1002/joc.1556.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelman, A., and D. B. Rubin, 1992: Inference from iterative simulation using multiple sequences. Stat. Sci., 7, 457511, https://doi.org/10.1214/ss/1177011136.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelman, A., D. Lee, and J. Guo, 2015: Stan: A probabilistic programming language for Bayesian inference and optimization. J. Educ. Behav. Stat., 40, 530543, https://doi.org/10.3102/1076998615606113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gershunov, A., and Coauthors, 2019: Precipitation regime change in Western North America: The role of atmospheric rivers. Sci. Rep., 9, 9944, https://doi.org/10.1038/s41598-019-46169-w.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haerter, J. O., and P. Berg, 2009: Unexpected rise in extreme precipitation caused by a shift in rain type? Nat. Geosci., 2, 372373, https://doi.org/10.1038/ngeo523.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, W., and Coauthors, 2014: Intensification of decadal and multi-decadal sea level variability in the western tropical Pacific during recent decades. Climate Dyn., 43, 13571379, https://doi.org/10.1007/s00382-013-1951-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawcroft, M., E. Walsh, K. Hodges, and G. Zappa, 2018: Significantly increased extreme precipitation expected in Europe and North America from extratropical cyclones. Environ. Res. Lett., 13, 124006, https://doi.org/10.1088/1748-9326/aaed59.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawkins, E., and R. Sutton, 2011: The potential to narrow uncertainty in projections of regional precipitation change. Climate Dyn., 37, 407418, https://doi.org/10.1007/s00382-010-0810-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haylock, M. R., G. C. Cawley, C. Harpham, R. L. Wilby, and C. M. Goodess, 2006: Downscaling heavy precipitation over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarios. Int. J. Climatol., 26, 13971415, https://doi.org/10.1002/joc.1318.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Howarth, M. E., C. D. Thorncroft, and L. F. Bosart, 2019: Changes in extreme precipitation in the northeast United States: 1979–2014. J. Hydrometeor., 20, 673689, https://doi.org/10.1175/JHM-D-18-0155.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hundecha, Y., and A. Bárdossy, 2008: Statistical downscaling of extremes of daily precipitation and temperature and construction of their future scenarios. Int. J. Climatol., 28, 589610, https://doi.org/10.1002/joc.1563.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Innocenti, S., A. Mailhot, and A. Frigon, 2017: Simple scaling of extreme precipitation in North America. Hydrol. Earth Syst. Sci., 21, 58235846, https://doi.org/10.5194/hess-21-5823-2017.

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

  • Ivancic, T. J., and S. B. Shaw, 2016: A U.S.-based analysis of the ability of the Clausius-Clapeyron relationship to explain changes in extreme rainfall with changing temperature. J. Geophys. Res. Atmos., 121, 30663078, https://doi.org/10.1002/2015JD024288.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, C., F. Giorgi, and G. Asrar, 2011: The Coordinated Regional Downscaling Experiment: CORDEX; an international downscaling link to CMIP5. CLIVAR Exchanges, No. 57, International CLIVAR Project Office, Southampton, United Kingdom, 3440, https://www.clivar.org/node/237.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., and J. Sedláček, 2013: Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Climate Change, 3, 369373, https://doi.org/10.1038/nclimate1716.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation CMIP5 and how we got there. Geophys. Res. Lett., 40, 11941199, https://doi.org/10.1002/grl.50256.

    • 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.

  • Lafon, T., S. Dadson, G. Buys, and C. Prudhomme, 2013: Bias correction of daily precipitation simulated by a regional climate model: A comparison of methods. Int. J. Climatol., 33, 13671381, https://doi.org/10.1002/joc.3518.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langhans, W., J. Schmidli, O. Fuhrer, S. Bieri, and C. Schär, 2013: Long-term simulations of thermally driven flows and orographic convection at convection-parameterizing and cloud-resolving resolutions. J. Appl. Meteor. Climatol., 52, 14901510, https://doi.org/10.1175/JAMC-D-12-0167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawrence, M. G., 2005: The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications. Bull. Amer. Meteor. Soc., 86, 225234, https://doi.org/10.1175/BAMS-86-2-225.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lehner, F., C. Deser, N. Maher, J. Marotzke, E. M. Fischer, L. Brunner, R. Knutti, and E. Hawkins, 2020: Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6. Earth Syst. Dyn., 11, 491508, https://doi.org/10.5194/esd-11-491-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lenderink, G., H. de Vries, H. J. Fowler, R. Barbero, B. van Ulft, and E. van Meijgaard, 2021: Scaling and responses of extreme hourly precipitation in three climate experiments with a convection-permitting model. Philos. Trans. Roy. Soc. London, A379, 20190544, https://doi.org/10.1098/rsta.2019.0544.

    • Search Google Scholar
    • Export Citation
  • Lepore, C., D. Veneziano, and A. Molini, 2014: 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
  • Lepore, C., J. T. Allen, and M. K. Tippett, 2016: Relationships between hourly rainfall intensity and atmospheric variables over the contiguous United States. J. Climate, 29, 31813197, https://doi.org/10.1175/JCLI-D-15-0331.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, C., F. Zwiers, X. Zhang, and G. Li, 2019: How much information is required to well constrain local estimates of future precipitation extremes? Earth’s Future, 7, 1124, https://doi.org/10.1029/2018EF001001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Z., 2012: Dynamics of interdecadal climate variability: A historical perspective. J. Climate, 25, 19631995, https://doi.org/10.1175/2011JCLI3980.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Livneh, B., T. J. Bohn, D. W. Pierce, F. Munoz-Arriola, B. Nijssen, R. Vose, D. R. Cayan, and L. Brekke, 2015: A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and southern Canada 1950–2013. Sci. Data, 2, 150042, https://doi.org/10.1038/sdata.2015.42.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lochbihler, K., G. Lenderink, and A. P. Siebesma, 2017: The spatial extent of rainfall events and its relation to precipitation scaling. Geophys. Res. Lett., 44, 86298636, https://doi.org/10.1002/2017GL074857.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lopez‐Cantu, T., A. F. Prein, and C. Samaras, 2020: Uncertainties in future U.S. extreme precipitation from downscaled climate projections. Geophys. Res. Lett., 47, e2019GL086797, https://doi.org/10.1029/2019GL086797.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madsen, H., D. Lawrence, M. Lang, M. Martinkova, and T. R. Kjeldsen, 2014: Review of trend analysis and climate change projections of extreme precipitation and floods in Europe. J. Hydrol., 519, 36343650, https://doi.org/10.1016/j.jhydrol.2014.11.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Magan, B., S. Kim, C. Wasko, R. Barbero, V. Moron, R. Nathan, and A. Sharma, 2020: Impact of atmospheric circulation on the rainfall-temperature relationship in Australia. Environ. Res. Lett., 15, 094098, https://doi.org/10.1088/1748-9326/abab35.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manola, I., B. V. D. Hurk, H. D. Moel, and J. C. Aerts, 2018: Future extreme precipitation intensities based on a historic event. Hydrol. Earth Syst. Sci., 22, 37773788, https://doi.org/10.5194/hess-22-3777-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maraun, D., and Coauthors, 2017: Towards process-informed bias correction of climate change simulations. Nat. Climate Change, 7, 764773, https://doi.org/10.1038/nclimate3418.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martinkova, M., and J. Kysely, 2020: Overview of observed Clausius-Clapeyron scaling of extreme precipitation in Midlatitudes. Atmosphere, 11, 786, https://doi.org/10.3390/atmos11080786.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., T. Das, and D. R. Cayan, 2013: Errors in climate model daily precipitation and temperature output: Time invariance and implications for bias correction. Hydrol. Earth Syst. Sci., 17, 21472159, https://doi.org/10.5194/hess-17-2147-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McAvaney, B., and Coauthors, 2001: Model evaluation. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 881 pp.

    • Search Google Scholar
    • Export Citation
  • Mishra, V., J. M. Wallace, and D. P. Lettenmaier, 2012: Relationship between hourly extreme precipitation and local air temperature in the United States. Geophys. Res. Lett., 39, L16403, https://doi.org/10.1029/2012GL052790.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molnar, P., S. Fatichi, L. Gaál, J. Szolgay, and P. Burlando, 2015: Storm type effects on super Clausius–Clapeyron scaling of intense rainstorm properties with air temperature. Hydrol. Earth Syst. Sci., 19, 17531766, https://doi.org/10.5194/hess-19-1753-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newman, P. A., L. Coy, S. Pawson, and L. R. Lait, 2016: The anomalous change in the QBO in 2015–2016. Geophys. Res. Lett., 43, 87918797, https://doi.org/10.1002/2016GL070373.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nissen, K. M., and U. Ulbrich, 2017: Increasing frequencies and changing characteristics of heavy precipitation events threatening infrastructure in Europe under climate change. Nat. Hazards Earth Syst. Sci., 17, 11771190, https://doi.org/10.5194/nhess-17-1177-2017.

    • Crossref
    • 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, 351363, https://doi.org/10.1007/s00382-006-0180-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, I., and S. Min, 2017: Role of convective precipitation in the relationship between subdaily extreme precipitation and temperature. J. Climate, 30, 95279537, https://doi.org/10.1175/JCLI-D-17-0075.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peleg, N., F. Marra, S. Fatichi, P. Molnar, E. Morin, A. Sharma, and P. Burlando, 2018: Intensification of convective rain cells at warmer temperatures observed from high-resolution weather radar data. J. Hydrometeor., 19, 715726, https://doi.org/10.1175/JHM-D-17-0158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pfahl, S., P. A. O’Gorman, and E. M. Fischer, 2017: Understanding the regional pattern of projected future changes in extreme precipitation. Nat. Climate Change, 7, 423427, https://doi.org/10.1038/nclimate3287.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pierce, D. W., D. R. Cayan, and B. L. Thrasher, 2014: Statistical downscaling using Localized Constructed Analogs (LOCA). J. Hydrometeor., 15, 25582585, https://doi.org/10.1175/JHM-D-14-0082.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., and Coauthors, 2015: A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges. Rev. Geophys., 53, 323361, https://doi.org/10.1002/2014RG000475.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., R. M. Rasmussen, K. Ikeda, C. Liu, M. P. Clark, and G. J. Holland, 2017: The future intensification of hourly precipitation extremes. Nat. Climate Change, 7, 4852, https://doi.org/10.1038/nclimate3168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pumo, D., G. Carlino, S. Blenkinsop, E. Arnone, H. Fowler, and L. V. Noto, 2019: Sensitivity of extreme rainfall to temperature in semi-arid Mediterranean regions. Atmos. Res., 225, 3044, https://doi.org/10.1016/j.atmosres.2019.03.036.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randall, D., M. Khairoutdinov, A. Arakawa, and W. Grabowski, 2003: Breaking the cloud parameterization deadlock. Bull. Amer. Meteor. Soc., 84, 15471564, https://doi.org/10.1175/BAMS-84-11-1547.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schleiss, M., 2018: How intermittency affects the rate at which rainfall extremes respond to changes in temperature. Earth Syst. Dyn., 9, 955968, https://doi.org/10.5194/esd-9-955-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schroeer, K., and G. Kirchengast, 2018: Sensitivity of extreme precipitation to temperature: The variability of scaling factors from a regional to local perspective. Climate Dyn., 50, 39813994, https://doi.org/10.1007/s00382-017-3857-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seager, R., N. Naik, and G. A. Vecchi, 2010: Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming. J. Climate, 23, 46514668, https://doi.org/10.1175/2010JCLI3655.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shaw, T. A., and Coauthors, 2016: Storm track processes and the opposing influences of climate change. Nat. Geosci., 9, 656664, https://doi.org/10.1038/ngeo2783.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shepherd, T. G., 2014: Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci., 7, 703708, https://doi.org/10.1038/ngeo2253.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, A., N. Lott, and R. Vose, 2011: The integrated surface database: Recent developments and partnerships. Bull. Amer. Meteor. Soc., 92, 704708, https://doi.org/10.1175/2011BAMS3015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steinschneider, S., R. McCrary, L. O. Mearns, and C. Brown, 2015: The effects of climate model similarity on probabilistic climate projections and the implications for local, risk-based adaptation planning. Geophys. Res. Lett., 42, 50145044, https://doi.org/10.1002/2015GL064529.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Q., F. Zwiers, X. Zhang, and G. Li, 2020: A comparison of intra-annual and long-term trend scaling of extreme precipitation with temperature in a large-ensemble regional climate simulation. J. Climate, 33, 92339245, https://doi.org/10.1175/JCLI-D-19-0920.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Themeßl, J. M., A. Gobiet, and A. Leuprecht, 2011: Empirical‐statistical downscaling and error correction of daily precipitation from regional climate models. Int. J. Climatol., 31, 15301544, https://doi.org/10.1002/joc.2168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thibeault, J. M., and A. Seth, 2015: Toward the credibility of northeast United States summer precipitation projections in CMIP5 and NARCCAP simulations. J. Geophys. Res. Atmos., 120, 10 05010 073, https://doi.org/10.1002/2015JD023177.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1999: Conceptual framework for changes of extremes of the hydrological cycle with climate change. Weather and Climate Extremes, Springer, 327339.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 2011: Changes in precipitation with climate change. Climate Res., 47, 123138, https://doi.org/10.3354/cr00953.

  • Tryhorn, L., and A. DeGaetano, 2011: A comparison of techniques for downscaling extreme precipitation over the northeastern United States. Int. J. Climatol., 31, 19751989, https://doi.org/10.1002/joc.2208.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Niekerk, A., J. F. Scinocca, and T. G. Shepherd, 2017: The modulation of stationary waves, and their response to climate change, by parameterized orographic drag. J. Atmos. Sci., 74, 25572574, https://doi.org/10.1175/JAS-D-17-0085.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Visser, J. B., C. Wasko, A. Sharma, and R. Nathan, 2020: Resolving inconsistencies in extreme precipitation‐temperature sensitivities. Geophys. Res. Lett., 47, e2020GL089723, https://doi.org/10.1029/2020GL089723.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, G., C. J. Kirchhoff, A. Seth, J. T. Abatzoglou, B. Livneh, D. W. Pierce, L. Fomenko, and T. Ding, 2020: Projected changes of precipitation characteristics depend on downscaling method and training data: MACA versus LOCA using the U.S. northeast as an example. J. Hydrometeor., 21, 27392758, https://doi.org/10.1175/JHM-D-19-0275.1.

    • 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
  • Wasko, C., and A. Sharma, 2014: Quantile regression for investigating scaling of extreme precipitation with temperature. Water Resour. Res., 50, 36083614, https://doi.org/10.1002/2013WR015194.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wasko, C., W. T. Lu, and R. Mehrotra, 2018: Relationship of extreme precipitation, dry-bulb temperature, and dew point temperature across Australia. Environ. Res. Lett., 13, 074031, https://doi.org/10.1088/1748-9326/aad135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Westra, S., L. V. Alexander, and F. W. Zwiers, 2013: Global increasing trends in annual maximum daily precipitation. J. Climate, 26, 39043918, https://doi.org/10.1175/JCLI-D-12-00502.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilby, R. L., T. M. L. Wigley, D. Conway, P. D. Jones, B. C. Hewitson, J. Main, and D. S. Wilks, 1998: Statistical downscaling of general circulation model output: A comparison of methods. Water Resour. Res., 34, 29953008, https://doi.org/10.1029/98WR02577.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woollings, T., A. Hannachi, and B. Hoskins, 2010: Variability of the North Atlantic eddy‐driven jet stream. Quart. J. Roy. Meteor. Soc., 136, 856868, https://doi.org/10.1002/qj.625.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, G., and K. Wang, 2021: Observed response of precipitation intensity to dew point temperature over the contiguous US. Theor. Appl. Climatol., 144, 13491362, https://doi.org/10.1007/s00704-021-03602-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zappa, G., L. C. Shaffrey, K. I. Hodges, P. G. Sansom, and D. B. Stephenson, 2013: A multimodel assessment of future projections of North Atlantic and European extratropical cyclones in the CMIP5 climate models. J. Climate, 26, 58465862, https://doi.org/10.1175/JCLI-D-12-00573.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., F. W. Zwiers, G. Li, H. Wan, and A. J. Cannon, 2017: Complexity in estimating past and future extreme short-duration rainfall. Nat. Geosci., 10, 255259, https://doi.org/10.1038/ngeo2911.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Observed and Projected Scaling of Daily Extreme Precipitation with Dew Point Temperature at Annual and Seasonal Scales across the Northeastern United States

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  • 1 aDepartment of Biological and Environmental Engineering, Cornell University, Ithaca, New York
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Abstract

This study investigates how extreme precipitation scales with dewpoint temperature across the northeastern United States, both in the observational record (1948–2020) and in a set of downscaled climate projections in the state of Massachusetts (2006–99). Spatiotemporal relationships between dewpoint temperature and extreme precipitation are assessed, and extreme precipitation–temperature scaling rates are evaluated on annual and seasonal scales using nonstationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% °C−1, but this varies by season, with most nonzero scaling rates in summer and fall and the largest rates (∼7.3% °C−1) in the summer. Dewpoint temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between −2.5% and 6.2% °C−1 at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dewpoint temperature over the twenty-first century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% °C−1). Overall, the observations suggest that extreme daily precipitation in the Northeast only thermodynamic scales with dewpoint temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.

Significance Statement

A warmer climate will likely result in the intensification of extreme precipitation, with the potential to enhance flood and stormwater risk. However, the relationship between extreme precipitation and temperature (i.e., the precipitation–temperature scaling rate) remains uncertain, particularly at regional scales, inhibiting societal adaptation to extreme events. Using observations and climate projections of daily precipitation and dewpoint temperature across the northeastern United States, we demonstrate that extreme daily precipitation does indeed scale with dewpoint temperature, but the rate of scaling varies by season, with the strongest relationship in the warm season.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Scott Steinschneider, ss3378@cornell.edu; Nasser Najibi, nn289@cornell.edu

Abstract

This study investigates how extreme precipitation scales with dewpoint temperature across the northeastern United States, both in the observational record (1948–2020) and in a set of downscaled climate projections in the state of Massachusetts (2006–99). Spatiotemporal relationships between dewpoint temperature and extreme precipitation are assessed, and extreme precipitation–temperature scaling rates are evaluated on annual and seasonal scales using nonstationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% °C−1, but this varies by season, with most nonzero scaling rates in summer and fall and the largest rates (∼7.3% °C−1) in the summer. Dewpoint temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between −2.5% and 6.2% °C−1 at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dewpoint temperature over the twenty-first century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% °C−1). Overall, the observations suggest that extreme daily precipitation in the Northeast only thermodynamic scales with dewpoint temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.

Significance Statement

A warmer climate will likely result in the intensification of extreme precipitation, with the potential to enhance flood and stormwater risk. However, the relationship between extreme precipitation and temperature (i.e., the precipitation–temperature scaling rate) remains uncertain, particularly at regional scales, inhibiting societal adaptation to extreme events. Using observations and climate projections of daily precipitation and dewpoint temperature across the northeastern United States, we demonstrate that extreme daily precipitation does indeed scale with dewpoint temperature, but the rate of scaling varies by season, with the strongest relationship in the warm season.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Scott Steinschneider, ss3378@cornell.edu; Nasser Najibi, nn289@cornell.edu

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