The climate system varies in complex ways across a wide range of timescales. Teleconnections—recurring patterns in the atmosphere and ocean that link remote regions—play a central role in shaping regional climate variability, predictability, and long-term change. Understanding and representing these links is therefore essential not only for advancing process understanding, but also for delivering robust and actionable regional climate information. However, given the large internal variability and strong external forcings involved, understanding the role of teleconnections in climate variability and change remains challenging. Both dynamical and modelling approaches, as well as statistical and other data-driven methods, have provided the foundation for many insights to date.

This session aims to bring together researchers using any combination of these approaches to study teleconnections across timescales, from synoptic to multi-decadal changes. In particular, we invite contributions that address one or more of the following topics: studies of the dynamics, variability, and predictability of teleconnections and their response to anthropogenic forcing; studies investigating and addressing model-observation discrepancies across the model hierarchy, from conventional to high-resolution and km-scale models and AI-based weather and climate models, as well as studies on regional impacts of teleconnections to assess physically consistent climate storylines and constrain projections.

We particularly encourage studies that bridge physical understanding and data-driven analysis, with an emphasis on physical interpretability and explainability. This includes theoretical advances and novel diagnostics for teleconnections, analysis of model experiments, as well as statistical and machine learning methods, including causal inference, Bayesian methods, and explainable or physics-informed deep learning.