Spatially explicit seasonal forecasting using fuzzy spatiotemporal clustering of long-term daily rainfall and temperature data

Papers published in Hydrology and Earth System Sciences Discussions are under open-access review for the journal Hydrology and Earth System Sciences Abstract A major limitation of statistical forecasts for specific weather station sites is that they are not spatial in the true sense. And while spatial predictions have been studied, their results have indicated a lack of seasonality. Global Circulation Models (GCMs) are spatial, but their spatial resolution is rather coarse. Here we propose spatially explicit 5 seasonal forecasting, based on the Fuzzy Classification of long-term (40 years) daily rainfall and temperature data to create climate memberships over time and location. Data were obtained from weather stations across southeast Australia, covering subtropical to arid climate zones. Class memberships were used to produce seasonal predictions using correlations with climate drivers and a regression rules approach. 10 Therefore, this model includes both local climate feedback and the continental drivers. The developed seasonal forecasting model predicts rainfall and temperature reasonably accurately. The final 6-month forecast for average maximum temperature and rainfall produced relative errors of 0.89 and 0.56 and Pearson correlation coefficients of 0.83 and 0.82, respectively.