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Global Circulation Models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, and including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern-scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in amount of warming over land and globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper we present a pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy balance model that calculates the amount of global warming. The set is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for CMIP5 ensemble. Critically it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impacts studies. Across models, the presented climate patterns represent consistent global mean trends, with maximum four GCMs exhibiting opposite sign of the trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and with decreased relative humidity. Overall, the patterns of the analysed variables explain one-third of regional change in decadal averages (mean Percentage Variance Explained, PVE, 34.25 ± 5.21), but signal in some models exhibits much more linearity (e.g. MIROC3.2(hires):41.53) than in others (GISS_ER: 22.67). The two most often considered variables: near-surface temperature and precipitation, have PVE of 85.44 ± 4.37 and 14.98 ± 4.61, respectively. The dataset is available for download and researchers in the areas of ecosystem modelling and climate change impact assessment are already starting to use it. Besides allowing time-efficient assessment for non-standard future scenarios of changed greenhouse gas (GHG) concentrations, it enables understanding of new representations of land surface processes, and including climate-carbon cycle feedbacks. Current and potential future applications of such modelling system are discussed. [ABSTRACT FROM AUTHOR]