Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions

AghaKouchak, 2018 Al-Yaari, 2019, Satellite-based soil moisture provides missing link between summertime precipitation and surface temperature biases in CMIP5 simulations over conterminous United States, Sci. Rep., 9, 1657, 10.1038/s41598-018-38309-5 Barry, 2018, West Africa climate extremes and climate change indices, Int. J. Climatol., 38, e921, 10.1002/joc.5420 Berg, 2013, Strong increase in convective precipitation in response to higher temperatures, Nat. Geosci., 6, 181, 10.1038/ngeo1731 Boucher Chen, 2008, Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res.: Atmosphere, 113 Chen, 2011, Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs, J. Clim., 24, 4741, 10.1175/2011JCLI4102.1 Collins, 2013, Long-term climate change: projections, commitments and irreversibility, 1029 Dai, 1999, Observed and model-simulated diurnal cycles of precipitation over the contiguous United States, J. Geophys. Res.: Atmosphere, 104, 6377, 10.1029/98JD02720 Daly, 2008, Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States, Int. J. Climatol., 28, 2031, 10.1002/joc.1688 Danabasoglu Danabasoglu Diaconescu, 2016, Evaluation of precipitation indices over North America from various configurations of regional climate models, Atmos.-Ocean, 54, 418, 10.1080/07055900.2016.1185005 Diaconescu, 2018, Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas, Clim. Dynam., 50, 2061, 10.1007/s00382-017-3736-4 Donat, 2013, Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset, J. Geophys. Res.: Atmosphere, 118, 10.1002/jgrd.50150 Donat, 2016, More extreme precipitation in the world's dry and wet regions, Nat. Clim. Change, 6, 508, 10.1038/nclimate2941 Donat, 2014, Consistency of temperature and precipitation extremes across various global gridded in situ and reanalysis datasets, J. Clim., 27, 5019, 10.1175/JCLI-D-13-00405.1 Easterling, 2017, 207 Easterling, 2016, Detection and attribution of climate extremes in the observed record, Weather and Climate Extremes, 11, 17, 10.1016/j.wace.2016.01.001 EC-Earth EC-Earth Edwards, 2011, History of climate modeling, WIREs Climate Change, 2, 128, 10.1002/wcc.95 Eyring, 2016, Overview of the coupled model Intercomparison project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev. (GMD), 9, 1937, 10.5194/gmd-9-1937-2016 Fischer, 2014, Detection of spatially aggregated changes in temperature and precipitation extremes, Geophys. Res. Lett., 41, 547, 10.1002/2013GL058499 Fischer, 2016, Observed heavy precipitation increase confirms theory and early models, Nat. Clim. Change, 6, 986, 10.1038/nclimate3110 Flato, 2014, Evaluation of climate models, 741 Gibson, 2019, Climate model evaluation in the presence of observational uncertainty: precipitation indices over the contiguous United States, J. Hydrometeorol., 20, 1339, 10.1175/JHM-D-18-0230.1 Gleckler, 2008, Performance metrics for climate models, J. Geophys. Res.: Atmosphere, 113, 1 Guo Herold, 2017, Large uncertainties in observed daily precipitation extremes over land, J. Geophys. Res.: Atmosphere, 122, 668, 10.1002/2016JD025842 Jiang, 2015, Extreme precipitation indices over China in CMIP5 models. Part I: model evaluation, J. Clim., 28, 8603, 10.1175/JCLI-D-15-0099.1 Jones, 1999, First- and second-order conservative remapping schemes for grids in spherical coordinates, Mon. Weather Rev., 127, 2204, 10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2 Kharin, 2007, Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations, J. Clim., 20, 1419, 10.1175/JCLI4066.1 Kharin, 2013, Changes in temperature and precipitation extremes in the CMIP5 ensemble, Climatic Change, 119, 345, 10.1007/s10584-013-0705-8 Knutti, 2010, Good practice guidance paper on assessing and combining multi model climate projections, 1 Knutti, 2013, Robustness and uncertainties in the new CMIP5 climate model projections, Nat. Clim. Change, 3, 369, 10.1038/nclimate1716 Kooperman, 2018, Rainfall from resolved rather than parameterized processes better represents the present-day and climate change response of moderate rates in the community atmosphere model, J. Adv. Model. Earth Syst., 10, 971, 10.1002/2017MS001188 Krasting Liang, 2007, Regional climate model simulation of US–Mexico summer precipitation using the optimal ensemble of two cumulus parameterizations, J. Clim., 20, 5201, 10.1175/JCLI4306.1 Lin, 2017, Causes of model dry and warm bias over central US and impact on climate projections, Nat. Commun., 8, 1, 10.1038/s41467-017-01040-2 Livneh, 2013, A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: update and extensions, J. Clim., 26, 9384, 10.1175/JCLI-D-12-00508.1 Miao, 2014, Assessment of CMIP5 climate models and projected temperature changes over northern eurasia, Environ. Res. Lett., 9, 10.1088/1748-9326/9/5/055007 Mondal, 2015, On the detection of human influence in extreme precipitation over India, J. Hydrol., 529, 1161, 10.1016/j.jhydrol.2015.09.030 Niu, 2018, Ensemble evaluation and projection of climate extremes in China using RMIP models, Int. J. Climatol., 38, 2039, 10.1002/joc.5315 Park, 2016, Evaluation of multiple regional climate models for summer climate extremes over East Asia, Clim. Dynam., 46, 2469, 10.1007/s00382-015-2713-z Park Prein, 2017, The future intensification of hourly precipitation extremes, Nat. Clim. Change, 7, 48, 10.1038/nclimate3168 Ragno, 2018, Quantifying changes in future intensity-duration-frequency curves using multimodel ensemble simulations, Water Resour. Res., 54, 1751, 10.1002/2017WR021975 Rajczak, 2017, Projections of future precipitation extremes over Europe: a multimodel assessment of climate simulations, J. Geophys. Res.: Atmosphere, 122, 10.1002/2017JD027176 Reidmiller, 2018 Ridley Seferian Sillmann, 2013, Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate, J. Geophys. Res.: Atmosphere, 118, 1716, 10.1002/jgrd.50203 Sillmann, 2013, Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections, J. Geophys. Res.: Atmosphere, 118, 2473, 10.1002/jgrd.50188 Stephens, 2010, Dreary state of precipitation in global models, J. Geophys. Res.: Atmosphere, 115 Stouffer, 2017, Cmip5 scientific gaps and recommendations for CMIP6, Bull. Am. Meteorol. Soc., 98, 95, 10.1175/BAMS-D-15-00013.1 Tang Taylor, 2012, An overview of CMIP5 and the experiment design, Bull. Am. Meteorol. Soc., 93, 485, 10.1175/BAMS-D-11-00094.1 Tebaldi, 2006, Going to the extremes, Climatic Change, 79, 185, 10.1007/s10584-006-9051-4 Vincent, 2018, Changes in Canada's climate: trends in indices based on daily temperature and precipitation data, Atmos.-Ocean, 56, 332, 10.1080/07055900.2018.1514579 Voldoire Wang, 2014, A global perspective on CMIP5 climate model biases, Nat. Clim. Change, 4, 201, 10.1038/nclimate2118 Wehner, 2014, The effect of horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5.1, J. Adv. Model. Earth Syst., 6, 980, 10.1002/2013MS000276 Westra, 2013, Global increasing trends in annual maximum daily precipitation, J. Clim., 26, 3904, 10.1175/JCLI-D-12-00502.1 van der Wiel, 2016, The resolution dependence of contiguous U.S. Precipitation extremes in response to CO2 forcing, J. Clim., 29, 7991, 10.1175/JCLI-D-16-0307.1 Wu Yukimoto Zhang, 2019, Constraining climate model projections of regional precipitation change, Geophys. Res. Lett., 46, 10522, 10.1029/2019GL083926 Zhang Zhang, 2011, Indices for monitoring changes in extremes based on daily temperature and precipitation data, WIREs Climate Change, 2, 851, 10.1002/wcc.147 Zhang, 2013, Attributing intensification of precipitation extremes to human influence, Geophys. Res. Lett., 40, 5252, 10.1002/grl.51010