Abstract. Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes.However, such high-resolution global simulations at climate timescales, with resolutions of at least 50 km in the atmosphere and 0.25 • in the ocean, have been performed at relatively few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs).Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability.The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950-2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. HighResMIP thereby focuses on one of the CMIP6 broad questions, "what are the origins and consequences of systematic model biases?", but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.

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[1] We use a very high resolution global climate model (~25 km grid size) with prescribed sea surface temperatures to show that greenhouse warming enhances the occurrence of hurricane-force (> 32.6 m s -1 ) storms over western Europe during early autumn (August-October), the majority of which originate as a tropical cyclone. The rise in Atlantic tropical sea surface temperatures extends eastward the breeding ground of tropical cyclones, yielding more frequent and intense hurricanes following pathways directed toward Europe. En route they transform into extratropical depressions and reintensify after merging with the midlatitude baroclinic unstable flow. Our model simulations clearly show that future tropical cyclones are more prone to hit western Europe, and do so earlier in the season, thereby increasing the frequency and impact of hurricane force winds. Citation: Haarsma, R. J., W.Hazeleger, C. Severijns, H. de Vries, A. Sterl, R. Bintanja, G. J. van Oldenborgh, and H. W. van den Brink (2013), More hurricanes to hit western Europe due to global warming, Geophys.

Decadal climate predictions may have skill due to predictable components in boundary conditions (mainly greenhouse gas concentrations but also tropospheric and stratospheric aerosol distributions) and initial conditions (mainly the ocean state). We investigate the skill of temperature and precipitation hindcasts from a multi-model ensemble of four climate forecast systems based on coupled ocean-atmosphere models. Regional variations in skill with and without trend are compared with similarly analysed uninitialised experiments to separate the trend due to monotonically increasing forcings from fluctuations around the trend due to the ocean initial state and aerosol forcings. In temperature most of the skill in both multi-model ensembles comes from the externally forced trends. The rise of the global mean temperature is represented well in the initialised hindcasts, but variations around the trend show little skill beyond the first year due to the absence of volcanic aerosols in the hindcasts and the unpredictability of ENSO. The models have non-trivial skill in hindcasts of North Atlantic sea surface temperature beyond the trend. This skill is highest in the northern North Atlantic in initialised experiments and in the subtropical North Atlantic in uninitialised simulations. A similar result is found in the Pacific Ocean, although the signal is less clear. The uninitialised simulations have good skill beyond the trend in the western North Pacific. The initialised experiments show some skill in the decadal ENSO region in the eastern Pacific, in agreement with previous studies. However, the results in this study are not statistically significant (p & 0.1) by themselves. The initialised models also show some skill in forecasting 4-year mean Sahel rainfall at lead times of 1 and 5 years, in agreement with the observed teleconnection from the Atlantic Ocean. Again, the skill is not statistically significant (p & 0.2). Furthermore, uninitialised simulations that include volcanic aerosols have similar skill. It is therefore still an open question whether initialisation improves predictions of Sahel rainfall. We conclude that the main source of skill in forecasting temperature is the trend forced by rising greenhouse gas concentrations. The ocean initial state contributes to skill in some regions, but variations in boundary forcings such as aerosols are as important in decadal forecasting.

Sea level rise, especially combined with possible changes in storm surges and increased river discharge resulting from climate change, poses a major threat in low-lying river deltas. In this study we focus on a specific example of such a delta: the Netherlands. To evaluate whether the country's flood protection strategy is capable of coping with future climate conditions, an assessment of low-probability/highimpact scenarios is conducted, focusing mainly on sea level rise. We develop a plausible high-end scenario of 0.55 to 1.15 m global mean sea level rise, and 0.40 to 1.05 m rise on the coast of the Netherlands by 2100 (excluding land subsidence), and more than three times these local values by 2200. Together with projections for changes in storm surge height and peak river discharge, these scenarios depict a complex, enhanced flood risk for the Dutch delta.

In the Essence project a 17‐member ensemble simulation of climate change in response to the SRES A1b scenario has been carried out using the ECHAM5/MPI‐OM climate model. The relatively large size of the ensemble makes it possible to accurately investigate changes in extreme values of climate variables. Here we focus on the annual‐maximum 2m‐temperature and fit a Generalized Extreme Value (GEV) distribution to the simulated values and investigate the development of the parameters of this distribution. Over most land areas both the location and the scale parameter increase. Consequently the 100‐year return values increase faster than the average temperatures. A comparison of simulated 100‐year return values for the present climate with observations (station data and reanalysis) shows that the ECHAM5/MPI‐OM model, as well as other models, overestimates extreme temperature values. After correcting for this bias, it still shows values in excess of 50°C in Australia, India, the Middle East, North Africa, the Sahel and equatorial and subtropical South America at the end of the century.

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