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Pattern scaling is a well-established method for approximating modeled spatial distributions of changes in temperature by assuming a time-invariant pattern that scales with changes in global mean temperature. We compare two methods of pattern scaling for annual mean precipitation (regression and epoch difference) and evaluate which method is <q>better</q> in particular circumstances by quantifying their robustness to interpolation/extrapolation in time, inter-model variations, and inter-scenario variations. Both the regression and epoch-difference methods (the two most commonly used methods of pattern scaling) have good absolute performance in reconstructing the climate model output, measured as an area-weighted root mean square error. We decompose the precipitation response in the RCP8.5 scenario into a CO₂ portion and a non-CO₂ portion. Extrapolating RCP8.5 patterns to reconstruct precipitation change in the RCP2.6 scenario results in large errors due to violations of pattern scaling assumptions when this CO₂-/non-CO₂-forcing decomposition is applied. The methodologies discussed in this paper can help provide precipitation fields to be utilized in other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change.