Statistical reconstructions of past climate variability based on climate indicators face several uncertainties: for instance, to what extent is the network of available proxy indicators dense enough for a meaningful estimation of past global temperatures?; can statistical models, calibrated with data at interannual timescales be used to estimate the low-frequency variability of the past climate?; and what is the influence of the limited spatial coverage of the instrumental records used to

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... ate the statistical models? Possible answers to these questions are searched by applying the statistical method of Mann et al. to a long control climate simulation as a climate surrogate. The role of the proxy indicators is played by the temperature simulated by the model at selected grid points. It is found that generally a set of a few tens of climate indicators is enough to provide a meaningful estimation (resolved variance of about 30%) of the simulated global annual temperature at annual timescales. The reconstructions based on around 10 indicators are barely able to resolve 10% of the temperature variance. The skill of the regression model increases at lower frequencies, so that at timescales longer than 20 yr the explained variance may reach 65%. However, the reconstructions tend to underestimate some periods of global cooling that are associated with temperatures anomalies off the Antarctic coast and south of Greenland lasting for about 20 yr. Also, it is found that in one 100-yr period, the low-frequency behavior of the global temperature evolution is not well reproduced, the error being probably related to tropical dynamics. This analysis could be influenced by the lack of a realistic variability of external forcing in the simulation and also by the quality of simulated key variability modes, such as ENSO. Both factors can affect the largescale coherence of the temperature field and, therefore, the skill of the statistical models.