Many approaches have been applied to forecast climate using statistical and deterministic models using independent and dependent variables empirically. It is more practical to analyze the parameters, but it needs validation anytime and anywhere. Kalman filtering unites physical and statistical model approaches to stochastic model renewable anytime for objective of on line forecasting. Based on research, sea surface temperature Nino 3.4 have high correlation with rainfall in Indonesia, so it is used to forecast rainfall in Cirebon as area study. Rainfall clustering in Cirebon results 6 groups with rainfall average 1400-1500 mm/year for dry area and 3000-3200 mm/year for wet area. Validation have correlation coefficient validation value more than 94%, correlation coefficient model value more than 78% and fit model value more than 38%. The result of regression gives R2 value of more than 0,8. It implies that predicting model using Kalman Filter is feasible to forecast montly rainfall based on sea surface temperature Nino 3.4. The result of rainfall prediction in Cirebon show increasing in rainfall until February 2005, with correlation coeficient value of model more than 90% and fit model more than 40%.