A climatic classification of the world’s wine regions

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) data reduction and cluster grouping similar regions. The PCA resulted in three components explaining 89 variation data, loadings differentiate between are warm/dry from cool/wet, low high diurnal temperature ranges, nighttime temperatures during ripening, vapour pressure deficits. analysis, based on these components, clusters defining globally, results showing premium can be found across each types. This is, to our knowledge, first such classification virtually all However, both change an increasing preference relative non-premium wines, many winegrowers may need their mixes varieties, or source more grapes appropriate climates.