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Detecting and explaining differences between palaeoclimates can provide valuable insights for Earth scientists investigating processes that are affected by climate change over geologic time. In this study, we describe and explain spatiotemporal patterns in palaeoclimate change that are relevant to Earth surface scientists. We apply a combination of multivariate cluster and discriminant analysis techniques to a set of high-resolution palaeoclimate simulations. The simulations were conducted with the ECHAM5 climate model and consistent setup. A pre-industrial (PI) climate simulation serves as the control experiment, which is compared to a suite of simulations of Late Cenozoic climates, namely a Mid-Holocene (MH, approximately 6.5 ka), Last Glacial Maximum (LGM, approximately 21 ka) and Pliocene (PLIO, approximately 3 Ma) climate. For each of the study regions (western South America, Europe, South Asia and southern Alaska), differences in climate are subjected to geographical clustering to identify dominant modes of climate change and their spatial extent for each time slice comparison (PI-MH, PI-LGM and PI-PLIO). The selection of climate variables for the cluster analysis is made on the basis of their relevance to Earth surface processes and includes 2 m air temperature, 2 m air temperature amplitude, consecutive freezing days, freeze-thaw days, maximum precipitation, consecutive wet days, consecutive dry days, zonal wind speed and meridional wind speed. We then apply a two-class multivariate discriminant analysis to simulation pairs PI-MH, PI-LGM and PI-PLIO to evaluate and explain the discriminability between climates within each of the anomaly clusters. Changes in ice cover create the most distinct and stable patterns of climate change, and create the best discriminability between climates in western Patagonia. The distinct nature of European palaeoclimates is statistically explained mostly by changes in 2 m air temperature (MH, LGM, PLIO), consecutive freezing days (LGM) and consecutive wet days (PLIO). These factors typically contribute 30 %-50 %, 10 %-40 % and 10 %-30 %, respectively, to climate discriminability. Finally, our results identify regions particularly prone to changes in precipitation-induced erosion and temperature-dependent physical weathering.