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. The extent to which climate conditions influenced the spatialdistribution of hominin populations in the past is highly debated.General circulation models (GCMs) and archaeological data have beenused to address this issue. Most GCMs are not currently capable ofsimulating past surface climate conditions with sufficientlydetailed spatial resolution to distinguish areas of potentialhominin habitat, however. In this paper, we propose a statisticaldownscaling method (SDM) for increasing the resolution of climatemodel outputs in a computationally efficient way. Our method uses ageneralised additive model (GAM), calibrated over present-dayclimatology data, to statistically downscale temperature andprecipitation time series from the outputs of a GCM simulating theclimate of the Last Glacial Maximum (19 000–23 000 BP) over westernEurope. Once the SDM is calibrated, we first interpolate thecoarse-scale GCM outputs to the final resolution and then use theGAM to compute surface air temperature and precipitation levelsusing these interpolated GCM outputs and fine-resolutiongeographical variables such as topography and distance from anocean. The GAM acts as a transfer function, capturing non-linearrelationships between variables at different spatial scales andcorrecting for the GCM biases. We tested three different techniquesfor the first interpolation of GCM output: bilinear, bicubic andkriging. The resulting SDMs were evaluated by comparing downscaledtemperature and precipitation at local sites with paleoclimatereconstructions based on paleoclimate archives (archaeozoologicaland palynological data) and the impact of the interpolationtechnique on patterns of variability was explored. The SDM based onkriging interpolation, providing the best accuracy, was thenvalidated on present-day data outside of the calibration period. Ourresults show that the downscaled temperature and precipitationvalues are in good agreement with paleoclimate reconstructions atlocal sites, and that our method for producing fine-grainedpaleoclimate simulations is therefore suitable for conductingpaleo-anthropological research. It is nonetheless important tocalibrate the GAM on a range of data encompassing the data to bedownscaled. Otherwise, the SDM is likely to overcorrect thecoarse-grain data. In addition, the bilinear and bicubicinterpolation techniques were shown to distort either the temporalvariability or the values of the response variables, while thekriging method offered the best compromise. Since climatevariability is an aspect of the environment to which humanpopulations may have responded in the past, the choice ofinterpolation technique is therefore an important consideration.

Comparison of spatial downscaling methods of general circulation model results to study climate variability during the Last Glacial Maximum