Accurate predictions of geophysical fluid have enormous social and economic values but remain to have significant uncertainties at different time and spatial scales. Although some dynamical, statistical, and their combined (“scholastic”) approaches were often used to make predictions and showed their respective usefulness, there exist great limitations in improving prediction level. This session will bring together experts to jointly address new approaches to predictions of geophysical fluid and to identification and quantification of uncertainties associated with predictability, and create an exchange of ideas likely to advance the state of predictions. Papers are invited on all aspects of conventional dynamical and statistical approaches to predictions and predictability estimation, and underlying that justification of the appropriateness of the use of any of them in a particular situation is particularly welcome. Papers on techniques that combine the dynamical and statistical approaches with newly emerging techniques of machine learning are also welcome.