Using a dataset with 16 climate variables for locations representing 813 wine regions that cover 99% of the world's winegrape area, we employ principal component analysis (PCA) for data reduction and cluster analysis for grouping similar regions. The PCA resulted in three components explaining 89% of the variation in the data, with loadings that differentiate between locations that are warm/dry from cool/wet, low from high diurnal temperature ranges, low from high nighttime temperatures during ripening, and low from high vapour pressure deficits. The cluster analysis, based on these three principal components, resulted in three clusters defining wine regions globally, with the results showing that premium wine regions can be found across each of the climate types. This is, to our knowledge, the first such classification of virtually all of the worldâ€™s wine regions. However, with both climate change and an increasing preference for premium relative to non-premium wines, many of the world's winegrowers may need to change their mixes of varieties, or source more of their grapes from more appropriate climates.