The use of regional climate model (RCM)‐based projections for providing regional climate information in a research and climate service contexts is currently expanding very fast. This has been possible thanks to a considerable effort in developing comprehensive ensembles of RCM projections, especially for Europe, in the EURO‐CORDEX community (Jacob et al., 2014, 2020). As of end of 2019, EURO‐CORDEX has developed a set of 55 historical and scenario projections (RCP8.5) using 8 driving global climate models (GCMs) and 11 RCMs. This article presents the ensemble including its design. We target the analysis to better characterize the quality of the RCMs by providing an evaluation of these RCM simulations over a number of classical climate variables and extreme and impact‐oriented indices for the period 1981–2010. For the main variables, the model simulations generally agree with observations and reanalyses. However, several systematic biases are found as well, with shared responsibilities among RCMs and GCMs: Simulations are overall too cold, too wet, and too windy compared to available observations or reanalyses. Some simulations show strong systematic biases on temperature, others on precipitation or dynamical variables, but none of the models/simulations can be defined as the best or the worst on all criteria. The article aims at supporting a proper use of these simulations within a climate services context.

This paper analyzes the ensemble of regional climate model (RCM) projections for Europe completed within the EURO‐CORDEX project. Projections are available for the two greenhouse gas concentration scenarios RCP2.6 (22 members) and RCP8.5 (55 members) at 0.11° resolution from 11 RCMs driven by eight global climate models (GCMs). The RCM ensemble results are compared with the driving CMIP5 global models but also with a subset of available last generation CMIP6 projections. Maximum warming is projected by all ensembles in Northern Europe in winter, along with a maximum precipitation increase there; in summer, maximum warming occurs in the Mediterranean and Southern European regions associated with a maximum precipitation decrease. The CMIP6 ensemble shows the largest signals, both for temperature and precipitation, along with the largest inter‐model spread. There is a high model consensus across the ensembles on an increase of extreme precipitation and drought frequency in the Mediterranean region. Extreme temperature indices show an increase of heat extremes and a decrease of cold extremes, with CMIP6 showing the highest values and EURO‐CORDEX the finest spatial details. This data set of unprecedented size and quality will provide the basis for impact assessment and climate service activities for the European region.

This article describes the development and evaluation of the U.K.’s new High-Resolution Global Environmental Model (HiGEM), which is based on the latest climate configuration of the Met Office Unified Model, known as the Hadley Centre Global Environmental Model, version 1 (HadGEM1). In HiGEM, the horizontal resolution has been increased to 0.83° latitude × 1.25° longitude for the atmosphere, and 1/3° × 1/3° globally for the ocean. Multidecadal integrations of HiGEM, and the lower-resolution HadGEM, are used to explore the impact of resolution on the fidelity of climate simulations.Generally, SST errors are reduced in HiGEM. Cold SST errors associated with the path of the North Atlantic drift improve, and warm SST errors are reduced in upwelling stratocumulus regions where the simulation of low-level cloud is better at higher resolution. The ocean model in HiGEM allows ocean eddies to be partially resolved, which dramatically improves the representation of sea surface height variability. In the Southern Ocean, most of the heat transports in HiGEM is achieved by resolved eddy motions, which replaces the parameterized eddy heat transport in the lower-resolution model. HiGEM is also able to more realistically simulate small-scale features in the wind stress curl around islands and oceanic SST fronts, which may have implications for oceanic upwelling and ocean biology.Higher resolution in both the atmosphere and the ocean allows coupling to occur on small spatial scales. In particular, the small-scale interaction recently seen in satellite imagery between the atmosphere and tropical instability waves in the tropical Pacific Ocean is realistically captured in HiGEM. Tropical instability waves play a role in improving the simulation of the mean state of the tropical Pacific, which has important implications for climate variability. In particular, all aspects of the simulation of ENSO (spatial patterns, the time scales at which ENSO occurs, and global teleconnections) are much improved in HiGEM.

The ability to run general circulation models (GCMs) at ever-higher horizontal resolutions has meant that tropical cyclone simulations are increasingly credible. A hierarchy of atmosphere-only GCMs, based on the Hadley Centre Global Environmental Model version 1 (HadGEM1) with horizontal resolution increasing from approximately 270 to 60 km at 50°N, is used to systematically investigate the impact of spatial resolution on the simulation of global tropical cyclone activity, independent of model formulation. Tropical cyclones are extracted from ensemble simulations and reanalyses of comparable resolutions using a feature-tracking algorithm. Resolution is critical for simulating storm intensity and convergence to observed storm intensities is not achieved with the model hierarchy. Resolution is less critical for simulating the annual number of tropical cyclones and their geographical distribution, which are well captured at resolutions of 135 km or higher, particularly for Northern Hemisphere basins. Simulating the interannual variability of storm occurrence requires resolutions of 100 km or higher; however, the level of skill is basin dependent. Higher resolution GCMs are increasingly able to capture the interannual variability of the large-scale environmental conditions that contribute to tropical cyclogenesis. Different environmental factors contribute to the interannual variability of tropical cyclones in the different basins: in the North Atlantic basin the vertical wind shear, potential intensity, and low-level absolute vorticity are dominant, whereas in the North Pacific basins midlevel relative humidity and low-level absolute vorticity are dominant. Model resolution is crucial for a realistic simulation of tropical cyclone behavior, and high-resolution GCMs are found to be valuable tools for investigating the global location and frequency of tropical cyclones.

The time scales of the Paris Climate Agreement indicate urgent action is required on climate policies over the next few decades, in order to avoid the worst risks posed by climate change. On these relatively short time scales the combined effect of climate variability and change are both key drivers of extreme events, with decadal time scales also important for infrastructure planning. Hence, in order to assess climate risk on such time scales, we require climate models to be able to represent key aspects of both internally driven climate variability and the response to changing forcings. In this paper we argue that we now have the modeling capability to address these requirements—specifically with global models having horizontal resolutions considerably enhanced from those typically used in previous Intergovernmental Panel on Climate Change (IPCC) and Coupled Model Intercomparison Project (CMIP) exercises. The improved representation of weather and climate processes in such models underpins our enhanced confidence in predictions and projections, as well as providing improved forcing to regional models, which are better able to represent local-scale extremes (such as convective precipitation). We choose the global water cycle as an illustrative example because it is governed by a chain of processes for which there is growing evidence of the benefits of higher resolution. At the same time it comprises key processes involved in many of the expected future climate extremes (e.g., flooding, drought, tropical and midlatitude storms).