In recent years, a growing number of modelling groups have created large initial condition ensembles for global climate models as well as high-resolution downscaling of such ensembles using regional climate models. These large ensembles offer new opportunities to quantify a model's true forced response and internal variability and improve our understanding of projected future changes. The ensembles also allow a reinterpretation of the observed record and help in interpreting sparse observations on the one hand and the assessment of model biases on the other hand. Furthermore, the large sample size enables the exploration of extreme events in a changing climate, the detection of compound events, as well as a robust assessment of a model's variability.

These large ensembles offer unexploited opportunities for a wide range of disciplines, such as hydrology and biogeosciences, and open new perspectives for climate dynamics. This issue is open to submissions which exploit these new opportunities and explore how a combined analysis of the existing large ensembles can advance our knowledge in different fields. We particularly invite submissions that use new methods to investigate these topics.

This issue arises out of the sessions Large Ensemble Climate Model Simulations: Exploring Natural Variability, Change Signals and Impacts and 1st SMILE (Single Model Initial-Condition Large Ensemble) Modeling Group Meeting, both held at the EGU 2019, but is open for all submissions within its scope.