Oceanic-Atmospheric Teleconnection Patterns (OATPs) are large-scale climate patterns that link atmospheric and oceanic circulations over different regions of the Earth. These patterns play a crucial role in influencing global weather and climate variability. Accordingly, once the link between hydro-climatic events and climatic and oceanic parameters is established, it becomes possible to create an efficient risk management system aimed at reducing the negative effects of extreme conditions. A system was formulated in this research to forecast long-term Monthly Precipitation (MP) occurrences and drought events as either dry, wet, or normal for two stations located in northwestern Iran, namely Kermanshah and Tabriz. Furthermore, primary measures for modeling classified precipitation in Japan were also implemented as a secondary case study. In the face of the intricacies and uncertainties of hydro-climatic processes, Fuzzy Logic (FL) has emerged as a widely favored option for modeling and decision-making. However, conventional FL cannot address data reliability. This study introduces Z-numbers, which incorporate data restraint and reliability. According to the obtained results, demonstrates a significantly higher level of accuracy, averaging up to 146% and 590% improvement in predicting drought events for the Tabriz and Kermanshah station SPIs, respectively. The results additionally disclosed that the model based on Z-number exhibited a superior level of precision in forecasting lengthy precipitation happenings, with an average accuracy of 112% for the Tabriz station and 69% for the Kermanshah station, even when presented with unobserved test data.