Seasonal rainfall forecasts can have significant value for resources planning and management e.g., reservoir

operations, agricultural practices and flood emergency responses. To mitigate this, effective planning

and management of water resources is necessary. In the short term, this requires a good idea of the upcoming

season. In the long term, it needs realistic projections of scenarios of future variability and change.

In this paper, we analyzed 38 years of rainfall data in Khorasan-e Razavi province that is located in the

northeastern part of Iran situated at latitude-longitude pairs (34°-38°N , 56°- 62°E). We attempted to train

Mamdani Fuzzy Inference system based on Tele-connection synoptically patterns with 38 years of rainfall

data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data.

In this study, at the first step, the relationship between synoptically pattern variations including Sea Level

Pressure (SLP), Sea Surface Temperature (SST), Sea Surface Pressure Difference (SLP), Sea Surface

Temperature Difference (SST), air temperature at 700 hpa, thickness between 500and 1000 hpa level, relative

humidity at 300 hpa and Precipitable water have been investigated. In the second step, model was

calibrated from 1970 to 1997. Finally, rainfall prediction is performed from 1998 to 2007. Simulation

results reveal that Mamdani Fuzzy Inference system techniques and regression models are promising and

efficient. Root mean square for Mamdani fuzzy inference system model and regression model was

obtained 6.34 and 5.5 millimeter, respectively.