The study was aimed at inferring spatial patterns of climatic zones as well as identifying significant discriminating bioclimatic controls for distribution of major ecosystems in Turkey, based on multivariate analyses. A total of 12 climate variables and 11 bioclimatic indices for the period of 1968-2004 at 272 meteorological stations, and four location data (latitudes, longitudes, altitudes, and distance to sea) were analyzed using discriminant analysis (DA), hierarchical and non-hierarchical cluster analyses (CA), principal components analysis (PCA), and multiple linear regression (MLR) models. The first three and four linear discriminant functions (LDFs) explained 88 and 95% of the variation in the dataset, respectively. The efficacy of the discriminant model was high (85.5%) based on the cross-validation method. The hierarchical and non-hierarchical CA pointed to seven clusters (climate types) that can be observed on the basis of broad climatic similarity of 97%. PCA elucidated 78% of variation in the dataset. MLR models that accounted for variations in the 12 climatic response variables as a function of the four location variables and aspect had R-2 values ranging from 28.8% for precipitation to 89.8% for mean air temperature and soil temperature for a depth of 5 cm. The multivariate analyses indicated that the meteorological stations are heterogeneous clusters consisting of the seven climatic zones. However, differences in the bioclimatic variables at the boundaries complicate the natural clustering scheme of a multidimensional cloud of data points and were detected in a climatologically plausible manner by the Ward and K-means CA, and PCA. Our multivariate approach revealed that the commonly used climatic zones are insufficient representations of the inferred climatic zones: (1) the coastal Black Sea; (2) the inland Black Sea; (3) the southeastern Anatolia; (4) the eastern Anatolia; (5) the central Anatolia; (6) the Mediterranean; and (7) the Aegean.