The aim of this study is to demonstrate the relationship between the long years' monthly average (LYMA) land surface temperature (LST) and the LYMA air temperature (T-a), the total precipitation (P-t), and the relative humidity (RH). Data from 27 meteorological stations in the Eastern Thrace region and corresponding thermal infrared images from Landsat-5 (TM) and Landsat-7 (ETM+) were used in this study. Simple regression models were developed for each meteorological station to predict the LYMA T-a, P-t and RH based on the LST values. The resulting LST-based prediction models were judged based on the correlation coefficient (r) and root mean square (RMSE). The average correlation and RMSE for the LST-based T-a were r = 0.959 and RMSE = 1.771 degrees C. The average correlation and RMSE for the LST-based P-t were r = -0.863 and RMSE = 10.098 mm. The average correlation and RMSE for the LST-based RH were r = -0.932 and RMSE = 1.875%. The results indicate that LST can be a good estimator for LYMA T-a, P-t and RH, and LYMA T-a is positively, LYMA P-t and LYMA RH are negatively correlated with LYMA LST.