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Cyclicality is a phenomenon commonly observed in nature, often in relation to natural events, including weather. Can cyclicality be used, however, to accurately and reliably predict long-range weather? For the past 30-plus years what appears to be a regularly cycling pattern has been investigated, researched, and tested in the development of a forecast system designed to make weather predictions using knowledge of this cycling pattern. Going back further, this may have been discovered as early as the 1940s. Long-range weather forecasting using this methodology is currently being utilized with accurate predictions of weather that is experienced at the surface from the next day to a likely limit of 300 days into the future. This method introduced in this study has demonstrable accuracy and robustness from December to September within a given forecast year. If this hypothesis of cyclicality plays an important role in weather forecasting, this seminal methodology represents a paradigm shift from current weather forecasting methods. Specific examples of cyclicality in the 500-hPa height fields from the 2016-2017 season will be showcased. For example, it will be shown how the 500-hPa height fields and surface weather can be accurately predicted months in advance based on how the weather pattern set up and cycled in the early fall. Specifically, this Cycling Pattern Hypothesis will be applied to the potential cyclicality of extreme precipitation events in the Lake Tahoe, NV (USA) area during the drought ending 2016-2017 season over the western United States. This new hypothesis may provide answers and solutions to forecasting droughts, floods, and more.