Results of fuzzy-rule-based modeling are presented for linking atmospheric circulation patterns (CP) and El Niño- Southern Oscillation (ENSO) to local climatic factors with the ultimate goal of long-term forecasting. Two important climatic factors will be considered. These are monthly precipitation and drought index over three remote locations of different climate: Arizona, Nebraska, and Hungary. CP is represented by the daily geopotential height maps classified either with cluster analysis using the k-means method used in Arizona and Nebraska, or the Hess and Brezowsky scheme for the European continent. The ENSO phenomena are represented by time series of the Southern Oscillation Index (SOI). In every case, the fuzzy-rule-based approach reproduces the statistical properties of monthly precipitation and drought index. The best results require considering the joint forcing of CP and ENSO information. Separate use of either the relative frequencies of CP types as premises or the lagged SOI shows that neither formulation can reproduce the empirical frequency distributions. Statistical measures of dependence between CP, ENSO, and the precipitation/drought index are relatively weak, precluding the use of other techniques such as multivariate regression. In every case, the calculated time series reproduces the observed time series for the calibration period. In Arizona the calculated precipitation time series, and in Nebraska the calculated drought index reproduces fairly well the observed time series even for the validation periods. In Hungary the observed time series for the validation periods are not reproduced.