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A stepwise multi regression-based statistics was employed for prioritizing the influence of several factors, anthropogenic and/or natural, on the ERA15 temperature increments. The 5 factors that are defined as predictors are: topography, aerosol index (TOMS-AI), tropospheric vertical velocity along with two anthropogenic factors, population density and land use changes (Land Use Change Index (LUCI) and Normalized Difference Vegetation Index (NDVI) trends). The seismic hazard assessment factor was also chosen as the “dummy variable” for validity. Special focus was given to the land use change factor, which was based on two different data sets; Human Impacts on Terrestrial Ecosystems (HITE) data of historical land use/land cover data and of NDVI trends during 1982 and 1991. The increment analysis updates of temperature, increments analysis update (IAU) (T), the predicted variable, was obtained from the ERA15 (1979–1993) reanalysis. The research consists of both spatial and vertical analyses, as well as the potential synergies of selected variables. The spatial geographic analysis is divided into three categories; (1) coarse region; (2) subregion analysis; and (c) a “small cell” of 4° × 4° analysis covering the global domain. It is shown that the following three factors, topography, TOMS-AI and NDVI, are statistically significant (at the p < 0.05 level) in the relationship with the IAU (T), which means that they are the most effective predictors of IAU (T), especially at the 700-hPa level during March–June. The 850-hPa level presents the weakest contribution to IAU (T), probably due to the contradicting influences of the various variables at this level. It was found that the land use effect, as expressed by the NDVI trends factor, shows a strong decrease with height and is one of the most influential near-surface factors over the East Mediterranean (EM), which explains up to 20% of the temperature increments in January at 700 hPa. Moreover, its influence is significant (p < 0.05) through all of the different stages of the multiple regression runs, a major finding not quantified earlier. The choice of monthly means was found to be not optimal, particularly for the tropospheric vertical velocity, due to the averaging of the synoptic systems within a month.
