The climate system is a complex network with nonlinear interactions on multiple spatial and temporal scales among multiple variables. Due to its complexity, evaluating climate predictability, predicting climate changes, and forewarning major climate events have been grand challenges for a long time. Despite the rapid progress in dynamic models in recent years, it is still challenging for the current generation of models to fully capture many of the complex features of the climate system, thus inducing uncertainties in climate prediction and early-warning techniques. In recent years, novel approaches from complex-systems science, dynamical systems, and nonlinear dynamics as well as emerging machine learning and artificial intelligence approaches have been shown to be powerful with respect to estimating climate predictability and improving predictive/early-warning skill regarding climate changes/climate events; however, the extent to which these new approaches can compensate for the current dynamic models and further enhance our understanding of the climate system remains an open question.

In order to summarize the recent progress and promote the use of novel approaches in climate predictability, prediction, and early-warning studies, we would like to propose a special issue entitled Emerging predictability, prediction, and early-warning approaches in climate science. The special issue is intended to bring together researchers interested in complex-systems science, tipping points, and predictability. All submissions within the scope of this special issue are welcome.