Davison, A. and Hinkley, D. V.: Bootstrap Methods and Their Applications,
Cambridge University Press, New York, NY, USA, 1997.
a,
b
Dee, D. P., Balmaseda, M., Balsamo, G., Engelen, R., Simmons, A. J., and
Thépaut, J.-N.: Toward a Consistent Reanalysis of the Climate System,
B. Am. Meteorol. Soc., 95, 1235–1248,
https://doi.org/10.1175/BAMS-D-13-00043.1, 2014.
a,
b
Déqué, M.: Frequency of precipitation and temperature extremes over France
in an anthropogenic scenario: Model results and statistical correction
according to observed values, Global Planet. Change, 57, 16–26,
https://doi.org/10.1016/j.gloplacha.2006.11.030, 2007.
a
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate
change projections: the role of internal variability, Clim. Dynam., 38,
527–546, 2012. a
Deser, C., Phillips, A. S., Alexander, M. A., and Smoliak, B. V.: Projecting
North American climate over the next 50 years: Uncertainty due to
internal variability, J. Climate, 27, 2271–2296, 2014. a
Diffenbaugh, N. S., Singh, D., Mankin, J. S., Horton, D. E., Swain, D. L.,
Touma, D., Charland, A., Liu, Y., Haugen, M., Tsiang, M., and Rajaratnam, B.: Quantifying
the influence of global warming on unprecedented extreme climate events,
P. Natl. Acad. Sci. USA, 114, 4881–4886, 2017. a
Dixon, K. W., Lanzante, J. R., Nath, M. J., Hayhoe, K., Stoner, A.,
Radhakrishnan, A., Balaji, V., and Gaitán, C. F.: Evaluating the
stationarity assumption in statistically downscaled climate projections: is
past performance an indicator of future results?, Clim. Change, 135,
395–408,
https://doi.org/10.1007/s10584-016-1598-0, 2016.
a,
b
Efron, B.: Nonparametric estimates of standard error: the jackknife, the
bootstrap and other methods, Biometrika, 68, 589–599, 1981. a
Fasiolo, M., Goude, Y., Nedellec, R., and Wood, S. N.: Fast calibrated
additive quantile regression, arXiv preprint arXiv:1707.03307, available at:
https://arxiv.org/abs/1707.03307 (last access: 1 April 2019), 2017. a
Forrester, A. I., Sóbester, A., and Keane, A. J.: Multi-fidelity
optimization via surrogate modelling, P. Roy. Soc.
A, 463, 3251–3269,
https://doi.org/10.1098/rspa.2007.1900, 2007.
a
Friederichs, P. and Hense, A.: Statistical downscaling of extreme precipitation
events using censored quantile regression, Mon. weather Rev., 135,
2365–2378, 2007. a
Friedman, J., Hastie, T., and Tibshirani, R.: The elements of statistical
learning, 10, Springer series in statistics, New York, NY, USA, 2001. a
Graham, L. P., Hagemann, S., Jaun, S., and Beniston, M.: On interpreting
hydrological change from regional climate models, Clim. Change, 81,
97–122,
https://doi.org/10.1007/s10584-006-9217-0, 2007.
a
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.:
Technical Note: Downscaling RCM precipitation to the station scale using
statistical transformations – a comparison of methods, Hydrol. Earth Syst.
Sci., 16, 3383–3390,
https://doi.org/10.5194/hess-16-3383-2012, 2012.
a
Haugen, M. A., Stein, M. L., Sriver, R. L., and Moyer, E. J.: Assessing changes
in temperature distributions using ensemble model simulations, J.
Climate, 31, 8573–8588,
https://doi.org/10.1175/JCLI-D-17-0782.1, 2018.
a,
b,
c,
d,
e,
f
Hayhoe, K., Cayan, D., Field, C. B., Frumhoff, P. C., Maurer, E. P., Miller,
N. L., Moser, S. C., Schneider, S. H., Cahill, K. N., Cleland, E. E., Dale,
L., Drapek, R., Hanemann, R. M., Kalkstein, L. S., Lenihan, J., Lunch, C. K.,
Neilson, R. P., Sheridan, S. C., and Verville, J. H.: Emissions pathways,
climate change, and impacts on California, P. Natl.
Acad. Sci., 101, 12422–12427,
https://doi.org/10.1073/pnas.0404500101, 2004.
a
He, X., Tuo, R., and Wu, C. F. J.: Optimization of multi-fidelity computer
experiments via the EQIE criterion, Technometrics, 59, 58–68,
https://doi.org/10.1080/00401706.2016.1142902, 2017.
a
Hogan, E. and Sriver, R.: Analyzing the effect of ocean internal variability on
depth-integrated steric sea-level rise trends using a low-resolution CESM
ensemble, Water, 9, 483,
https://doi.org/10.3390/w9070483, 2017.
a
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner,
P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., et al.: The
community earth system model: a framework for collaborative research,
B. Am. Meteorol. Soc., 94, 1339–1360, 2013.
a,
b
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) large ensemble project: A
community resource for studying climate change in the presence of internal
climate variability, B. Am. Meteorol. Soc., 96,
1333–1349, 2015. a
Koenker, R.: Quantile regression for longitudinal data, J. Multi.
Anal., 91, 74–89, 2004. a
Koenker, R.: quantreg: Quantile Regression, r package version 5.33, available
at:
https://CRAN.R-project.org/package=quantreg (last access:
1 February 2019), 2017. a
Koenker, R. and Schorfheide, F.: Quantile spline models for global temperature
change, Clim. Change, 28, 395–404, 1994.
a,
b
Le Gratiet, L. and Garnier, J.: Recursive co-kriging model for design of
computer experiments with multiple levels of fidelity, Int. J.
Uncertainty Quant., 4, 365–386, 2014. a
Leeds, W. B., Moyer, E. J., and Stein, M. L.: Simulation of future climate
under changing temporal covariance structures, Adv. Stat. Clim. Meteorol.
Oceanogr., 1, 1–14,
https://doi.org/10.5194/ascmo-1-1-2015, 2015.
a,
b,
c,
d,
e
Li, H., Sheffield, J., and Wood, E. F.: Bias correction of monthly
precipitation and temperature fields from Intergovernmental Panel on Climate
Change AR4 models using equidistant quantile matching, J. Geophys.
Res.-Atmos., 115, D10101,
https://doi.org/10.1029/2009JD012882, 2010.
a,
b,
c,
d,
e
Liu, Y. and Wu, Y.: Stepwise multiple quantile regression estimation using
non-crossing constraints, Stat. Interf., 2, 299–310, 2009.
a,
b
Maraun, D.: Nonstationarities of regional climate model biases in European
seasonal mean temperature and precipitation sums, Geophys. Res.
Lett., 39, L06706,
https://doi.org/10.1029/2012GL051210, 2012.
a
Maraun, D.: Bias correction, quantile mapping, and downscaling: revisiting the
inflation issue, J. Climate, 26, 2137–2143, 2013.
a,
b
McGinnis, S., Nychka, D., and Mearns, L. O.: A new distribution mapping
technique for climate model bias correction, in: Machine Learning and Data
Mining Approaches to Climate Science, 91–99, Springer Nature Switzerland AG,
Switzerland, 2015. a
McKinnon, K. A., Rhines, A., Tingley, M. P., and Huybers, P.: The changing
shape of Northern Hemisphere summer temperature distributions, J.
Geophys. Res.-Atmos., 121, 8849–8868, 2016. a
Miao, C., Su, L., Sun, Q., and Duan, Q.: A nonstationary bias-correction
technique to remove bias in GCM simulations, J. Geophys. Res.-Atmos., 121, 5718–5735, 2016. a
Oh, H.-S., Lee, T. C., and Nychka, D. W.: Fast nonparametric quantile
regression with arbitrary smoothing methods, J. Comput.
Graph. Stat., 20, 510–526, 2011.
a,
b
Olsson, J., Berggren, K., Olofsson, M., and Viklander, M.: Applying climate
model precipitation scenarios for urban hydrological assessment: A case study
in Kalmar City, Sweden, Atmos. Res., 92, 364–375, 2009. a
Piani, C., Weedon, G., Best, M., Gomes, S., Viterbo, P., Hagemann, S., and
Haerter, J.: Statistical bias correction of global simulated daily
precipitation and temperature for the application of hydrological models,
J. Hydrol., 395, 199–215, 2010. a
Poppick, A., Moyer, E. J., and Stein, M. L.: Estimating trends in the global
mean temperature record, Adv. Stat. Clim. Meteorol. Oceanogr., 3, 33–53,
https://doi.org/10.5194/ascmo-3-33-2017, 2017.
a,
b,
c,
d
Räisänen, J. and Räty, O.: Projections of daily mean temperature
variability in the future: cross-validation tests with ENSEMBLES regional
climate simulations, Clim. Dynam., 41, 1553–1568, 2013. a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
available at:
http://www.R-project.org/ (last access: 23 February 2019), 2013. a
Rhines, A., McKinnon, K. A., Tingley, M. P., and Huybers, P.: Seasonally
resolved distributional trends of North American temperatures show
contraction of winter variability, J. Climate, 30, 1139–1157, 2017. a
Sriver, R. L., Forest, C. E., and Keller, K.: Effects of initial conditions
uncertainty on regional climate variability: An analysis using a
low-resolution CESM ensemble, Geophys. Res. Lett., 42, 5468–5476,
2015.
a,
b,
c
Stoner, A. M., Hayhoe, K., Yang, X., and Wuebbles, D. J.: An asynchronous
regional regression model for statistical downscaling of daily climate
variables, Int. J. Climatol., 33, 2473–2494, 2013. a
Swain, D. L., Tsiang, M., Haugen, M., Singh, D., Charland, A., Rajaratnam, B.,
and Diffenbaugh, N. S.: The extraordinary
California drought of 2013/2014:
Character, context, and the role of climate change, B. Am.
Meteorol. Soc., 95, S3–S7, 2014. a
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model
simulations for hydrological climate-change impact studies: Review and
evaluation of different methods, J. Hydrol., 456, 12–29, 2012. a
Themeßl, M. J., Gobiet, A., and Heinrich, G.: Empirical-statistical
downscaling and error correction of regional climate models and its impact on
the climate change signal, Clim. Change, 112, 449–468, 2012.
a,
b
Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., et al.: The
representative concentration pathways: an overview, Clim. Change, 109,
https://doi.org/10.1007/s10584-011-0148-z,
2011.
a
Vrac, M., Stein, M., Hayhoe, K., and Liang, X.-Z.: A general method for
validating statistical downscaling methods under future climate change,
Geophys. Res. Lett., 34, L18701,
https://doi.org/10.1029/2007GL030295, 2007.
a
Wang, L. and Chen, W.: Equiratio cumulative distribution function matching as
an improvement to the equidistant approach in bias correction of
precipitation, Atmos. Sci. Lett., 15, 1–6, 2014.
a,
b
Watterson, I. G.: Calculation of probability density functions for temperature
and precipitation change under global warming, J. Geophys.
Res.-Atmos., 113, D12106,
https://doi.org/10.1029/2007JD009254, 2008.
a
Wei, Y., Pere, A., Koenker, R., and He, X.: Quantile regression methods for
reference growth charts, Stat. Med., 25, 1369–1382, 2006. a
Willems, P. and Vrac, M.: Statistical precipitation downscaling for small-scale
hydrological impact investigations of climate change, J. Hydrol.,
402, 193–205, 2011. a
Wood, A. W., Leung, L. R., Sridhar, V., and Lettenmaier, D.: Hydrologic
implications of dynamical and statistical approaches to downscaling climate
model outputs, Clim. Change, 62, 189–216, 2004. a
