Spatially and temporally dependent fingerprint patterns of near-surface temperature change are derived from transient climate simulations of the second Hadley Centre coupled ocean–atmosphere GCM (HADCM2). Trends in near-surface temperature are calculated from simulations in which HADCM2 is forced with historical increases in greenhouse gases only and with both greenhouse gases and anthropogenic sulfur emissions. For each response an ensemble of four simulations is carried out. An estimate of the natural internal variability of the ocean–atmosphere system is taken from a long multicentury control run of HADCM2. The aim of the study is to investigate the spatial and temporal scales on which it is possible to detect a significant change in climate. Temporal scales are determined by taking temperature trends over 10, 30, and 50 yr using annual mean data, and spatial scales are defined by projecting these trends onto spherical harmonics. Each fingerprint pattern is projected onto the recent observed pattern to give a scalar detection variable. This is compared with the distribution expected from natural variability, estimated by projecting the fingerprint pattern onto a distribution of patterns taken from the control run. Detection is claimed if the detection variable is greater than the 95th percentile of the distribution expected from natural variability. The results show that climate change can be detected on the global mean scale for 30- and 50-yr trends but not for 10-yr trends, assuming that the model’s estimate of variability is correct. At subglobal scales, climate change can be detected only for 50-yr trends and only for large spatial scales (greater than 5000 km). Patterns of near-surface temperature trends for the 50 yr up to 1995 from the simulation that includes only greenhouse gas forcing are inconsistent with the observed patterns at small spatial scales (less than 2000 km). In contrast, patterns of temperature trends for the simulation that includes both greenhouse gas and sulfate forcing are consistent with the observed patterns at all spatial scales. The possible limits to future detectability are investigated by taking one member of each ensemble to represent the observations and other members of the ensemble to represent model realizations of future temperature trends. The results show that for trends to 1995 the probability of detection is greatest at spatial scales greater than 5000 km. As the future signal of climate change becomes larger relative to the noise of natural variability, detection becomes very likely at all spatial scales by the middle of the next century. The model underestimates climate variability as seen in the observations at spatial scales less than 2000 km. Therefore, some caution must be exercised when interpreting model-based detection results that include a contribution of small spatial scales to the climate change fingerprint.