Homogenization of atmospheric variables to detect and attribute past and present climate trends and to predict scenarios of future meteorological extreme events is a crucial issue for the reliability of analysis results. Here we present a quality control and new homogenization method (PENHOM) based on a penalized log likelihood procedure and a nonlinear model applied to 174 daily summer maximum temperature series in the Greater Mediterranean Region covering the last 50–100 years. The break detection method does not rely on homogeneous reference stations and was chosen owing to the lack of metadata available. The correction procedure allows the higher-order moments of the candidate distribution to be corrected, which is important if the homogenized series are to be used to quantify temperature extremes. Both procedures require a set of highly correlated neighboring stations to correct climate series reliably. After carrying out the homogeneity procedure, 84% of all time series were found to contain at least one artificial breakpoint. Time series of the eastern Mediterranean (one breakpoint in 24 years on average) show significantly more breakpoints than do series of the Western Basin (one breakpoint in 36 years on average). The mean adjustment (standard error) of all daily summer maximum temperatures is +0.03°C (±0.38°C) for the western Mediterranean, +0.16°C (±0.52°C) for the central Mediterranean, and +0.19°C (±0.30°C) for the eastern Mediterranean, indicating a reduced increase in mean summer daytime temperature compared to that detected by analyzing raw data. The adjustments for higher-order moments were not uniform. Most significant mean changes due to homogenization were detected for both: the hottest (+0.15°C ± 0.66°C) and coldest decile (−0.83°C ± 1.28°C) compared to the raw data in the central Mediterranean. This study demonstrates that homogenization of daily temperature data is necessary before any analysis of temperature-related extreme events such as heat waves, cold spells, and their impacts on human health, agriculture, and ecosystems can be studied.