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A method, based on climate pattern scaling, has been developed to\udexpand a small number of projections of fields of a selected climate\udvariable (X) into an ensemble that encapsulates a wide range of\udindicative model structural uncertainties. The method described in\udthis paper is referred to as the Ensemble Projections Incorporating\udClimate model uncertainty (EPIC) method. Each ensemble member is\udconstructed by adding contributions from (1) a climatology derived\udfrom observations that represents the time-invariant part of the\udsignal; (2) a contribution from forced changes in X, where those\udchanges can be statistically related to changes in global mean\udsurface temperature (Tglobal); and (3) a contribution\udfrom unforced variability that is generated by a stochastic weather\udgenerator. The patterns of unforced variability are also allowed to\udrespond to changes in Tglobal. The statistical\udrelationships between changes in X (and its patterns of\udvariability) and Tglobal are obtained in a <q>training</q>\udphase. Then, in an <q>implementation</q> phase, 190 simulations of\udTglobal are generated using a simple climate model tuned\udto emulate 19 different global climate models (GCMs) and\ud10 different carbon cycle models. Using the generated\udTglobal time series and the correlation between the\udforced changes in X and Tglobal, obtained in the\ud<q>training</q> phase, the forced change in the X field can be\udgenerated many times using Monte Carlo analysis. A stochastic\udweather generator is used to generate realistic representations of\udweather which include spatial coherence. Because GCMs and regional\udclimate models (RCMs) are less likely to correctly represent\udunforced variability compared to observations, the stochastic\udweather generator takes as input measures of variability derived\udfrom observations, but also responds to forced changes in climate in\uda way that is consistent with the RCM projections. This approach to\udgenerating a large ensemble of projections is many orders of\udmagnitude more computationally efficient than running multiple GCM\udor RCM simulations. Such a large ensemble of projections permits\uda description of a probability density function (PDF) of future\udclimate states rather than a small number of individual story lines\udwithin that PDF, which may not be representative of the PDF as\uda whole; the EPIC method largely corrects for such potential\udsampling biases. The method is useful for providing projections of\udchanges in climate to users wishing to investigate the impacts and\udimplications of climate change in a probabilistic way. A web-based\udtool, using the EPIC method to provide probabilistic projections of\udchanges in daily maximum and minimum temperatures for New Zealand,\udhas been developed and is described in this paper

A method to encapsulate model structural uncertainty in ensemble projections of future climate: EPIC v1.0