Decadal predictions of temperature, precipitation and streamflow considering their covariability

The significance of decadal predictions of climate for successful adaptation is now well understood. The focus is quickly shifting from end of 21st century predictions to decadal predictions, given that they should be much better constrained by emissions scenarios and the high economic present value of capital investments in infrastructure and loss reduction over the next decade. We evaluate the efficacy of current seasonal to decadal (S2D) predictions of temperature, precipitation and streamflow in specific contexts, considering both the external forcing and internal dynamical variability at the relevant time scales. Both global and selected regional scales are considered. We compare and enhance the predictions from state of the art S2D models, the CMIP6 models using statistical/machine learning tools with model generated and empirical data. Insights from these experiments are presented. Acknowledgment: This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Water Management Program for Drought, funded by Korea Ministry of Environment (MOE) (2022003610003).