Precipitation patterns are influenced by many factors, such as global atmospheric circulations to name but one. Precipitation patterns in Iran have always had great fluctuations even in a smaller scale like the Alburz Mountain Range. The present research has tried to find the relationship between global atmospheric patterns and the pervasive precipitation ones in Alburz. For doing so, 17 climate indices have been chosen with the correlation between these indices and the precipitation data calculated in different lag times, using a backward correlation method (from the present time to 3 months earlier). Based on the obtained correlation results, a regression modeling has been conducted that employs a backward method. As for each lag time, one equation has been offered to estimate the amount of precipitation for every single region. Results have shown that the Bivariate ENSO Time Series (BEST) and the East Pacific Oscillation (EPO) provide the highest correlation with the pervasive precipitation time series. Also, it has been demonstrated that in multivariate correlation, the efficient index to model the relation among these indices as well as precipitation in southern Alburz alters in each lag time. Both MBE and RMSE, employed to evaluate the modeling, show relatively acceptable values, implying that the equations are acceptably capable of predicting the amount of precipitation in both northern and southern Alburz.