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A Climate Pattern-Scaling Model (CPSM) that simulates global patterns ofclimate change, for a prescribed emissions scenario, is described. A CPSMworks by quantitatively establishing the statistical relationship between aclimate variable at a specific location (e.g. daily maximum surfacetemperature, Tmax) and one or more predictor time series (e.g. globalmean surface temperature, Tglobal) – referred to as the"training" of the CPSM. This training uses a regression model to derive fitcoefficients that describe the statistical relationship between the predictortime series and the target climate variable time series. Once thatrelationship has been determined, and given the predictor time series for anygreenhouse gas (GHG) emissions scenario, the change in the climate variableof interest can be reconstructed – referred to as the "application" of theCPSM. The advantage of using a CPSM rather than a typical atmosphere–oceanglobal climate model (AOGCM) is that the predictor time series required bythe CPSM can usually be generated quickly using a simple climate model (SCM)for any prescribed GHG emissions scenario and then applied to generate globalfields of the climate variable of interest. The training can be performedeither on historical measurements or on output from an AOGCM. Using modeloutput from 21st century simulations has the advantage that the climatechange signal is more pronounced than in historical data and therefore a morerobust statistical relationship is obtained. The disadvantage of using AOGCMoutput is that the CPSM training might be compromised by any AOGCMinadequacies. For the purposes of exploring the various methodologicalaspects of the CPSM approach, AOGCM output was used in this study to trainthe CPSM. These investigations of the CPSM methodology focus on monthly meanfields of daily temperature extremes (Tmax and Tmin). Themethodological aspects of the CPSM explored in this study include(1) investigation of the advantage gained in having five predictor timeseries over having only one predictor time series, (2) investigation of thetime dependence of the fit coefficients and (3) investigation of thedependence of the fit coefficients on GHG emissions scenario. Key conclusionsare (1) overall, the CPSM trained on simulations based on the RepresentativeConcentration Pathway (RCP) 8.5 emissions scenario is able to reproduce AOGCMsimulations of Tmax and Tmin based on predictor time series froman RCP 4.5 emissions scenario; (2) access to hemisphere average land andocean temperatures as predictors improves the variance that can be explained,particularly over the oceans; (3) regression model fit coefficients derivedfrom individual simulations based on the RCP 2.6, 4.5 and 8.5 emissionsscenarios agree well over most regions of the globe (the Arctic is theexception); (4) training the CPSM on concatenated time series from anensemble of simulations does not result in fit coefficients that explainsignificantly more of the variance than an approach that weights resultsbased on single simulation fits; and (5) the inclusion of a linear timedependence in the regression model fit coefficients improves the varianceexplained, primarily over the oceans

Methodological aspects of a pattern-scaling approach to produce global fields of monthly means of daily maximum and minimum temperature