Abstract
In order to link the monthly areal precipitation to large‐scale circulation patterns, a fuzzy indexing technique is used in conjunction with a fuzzy rule‐based technique and also a standard linear regression. After clustering the lag‐correlation centers, fuzziness is introduced, and several representative indices of the monthly areal precipitation in Arizona are calculated and interpreted. The relation between the indices and the precipitation is analyzed to develop the fuzzy model and then a multivariate linear regression model. To measure the forecasting capability of the models, the data are divided into a calibration period (1947–79) and a validation period (1980–1988). A comparison of the results shows that the fuzzy rule‐based model performs better than the regression model and has potential for monthly precipitation forecasting. Moreover, an adaptive fuzzy rule‐based framework is described so that the model can be used under climate change.
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