Averaging results from multiple models has previously been found to improve estimates of the climatology and seasonal predictions of atmospheric variables. Here we describe how a multi-model mean of the simulated response to greenhouse gas and sulphate aerosol changes may be used to detect anthropogenic influence on surface temperature. The scaling factor on a combined greenhouse gas and sulphate aerosol response pattern is estimated using a five model ensemble, and is found to be similar to that estimated using individual models, with similar uncertainties. When applied to the simultaneous detection of separate greenhouse gas and sulphate aerosol responses, the multi-model method indicates a closer consistency between the observations and simulated responses, with reduced uncertainties. This improvement is at least in part due to the larger ensemble sizes and increased length of control integration available when data from multiple models are combined.