Climate change is expected to affect not only the means of climatic variables, but also their variabilities1,2 and extremes such as heat waves2–6. In particular, modelling studies have postulated a possible impact of soil-moisture deficit and drought on hot extremes7–11. Such effects could be responsible for impending changes in the occurrence of heat waves in Europe7. Here we analyse observational indices based on measurements at 275 meteorological stations in central and southeastern Europe, and on publicly available gridded observations12. We find a relationship between soil-moisture deficit, as expressed by the standardized precipitation index13, and summer hot extremes in southeastern Europe. This relationship is stronger for the high end of the distribution of temperature extremes. We compare our results with simulations of current climate models and find that the models correctly represent the soil-moisture impacts on temperature extremes in southeastern Europe, but overestimate them in central Europe. Given the memory associated with soil moisture storage, our findings may help with climate-changeadaptation measures, such as early-warning and prediction tools for extreme heat wave

[1] Within the framework of the European project ENSEMBLES (ensembles-based predictions of climate changes and their impacts) we explore the systematic bias in simulated monthly mean temperature and precipitation for an ensemble of thirteen regional climate models (RCMs). The models have been forced with the European Centre for Medium Range Weather Forecasting Reanalysis (ERA40) and are compared to a new high resolution gridded observational data set. We find that each model has a distinct systematic bias relating both temperature and precipitation bias to the observed mean. By excluding the twenty-five percent warmest and wettest months, respectively, we find that a derived second-order fit from the remaining months can be used to estimate the values of the excluded months. We demonstrate that the common assumption of bias cancellation (invariance) in climate change projections can have significant limitations when temperatures in the warmest months exceed 4-6°C above present day conditions. Citation: Christensen, J. H., F. Boberg, O. B. Christensen, and P. Lucas-Picher (2008), On the need for bias correction of regional climate change projections of temperature and precipitation, Geophys. Res. Lett., 35, L20709,

The European CORDEX (EURO-CORDEX) initiative is a large voluntary effort that seeks to advance regional climate and Earth system science in Europe. As part of the World Climate Research Programme (WCRP)-Coordinated Regional Downscaling Experiment (CORDEX), it shares the broader goals of providing a model evaluation and climate projection framework and improving communication with both the General Circulation Model (GCM) and climate data user communities. EURO-CORDEX oversees the design and coordination of ongoing ensembles of regional climate projections of unprecedented size and resolution (0.11 • EUR-11 and 0.44 • EUR-44 domains). Additionally, the inclusion of empiricalstatistical downscaling allows investigation of much larger multi-model ensembles. These complementary approaches provide a foundation for scientific studies within the climate research community and others. The value of the EURO-CORDEX ensemble is shown via numerous peer-reviewed studies and its use in the development of climate services. Evaluations of the EUR-44 and EUR-11 ensembles also show the benefits of higher resolution. However, significant challenges remain. To further advance scientific understanding, two flagship pilot studies (FPS) were initiated. The first investigates local-regional phenomena at convection-permitting scales over central Europe and the Mediterranean in collaboration with the Med-CORDEX community. The second investigates the impacts of land cover changes on European climate across spatial and temporal scales. Over the coming years, the EURO-CORDEX community looks forward to closer collaboration with other communities, new advances, supporting international initiatives such as the IPCC reports, and continuing to provide the basis for research on regional climate impacts and adaptation in Europe.

The paper reviews recent advances in studies of electric discharges in the stratosphere and mesosphere above thunderstorms, and their effects on the atmosphere. The primary focus is on the sprite discharge occurring in the mesosphere, which is the most commonly observed high altitude discharge by imaging cameras from the ground, but effects on the upper atmosphere by electromagnetic radiation from lightning are also considered. During the past few years, co-ordinated observations over Southern Europe have been made of a wide range of parameters related to sprites and their causative thunderstorms. Observations have been complemented by the modelling of processes ranging from the electric discharge to perturbations of trace gas concentrations in the upper atmosphere. Observations point to significant energy deposition by sprites in the neutral atmosphere as observed by infrasound waves detected at up to 1000 km distance, whereas elves and lightning have been shown significantly to affect ionization and heating of the lower ionosphere/mesosphere. Studies of the thunderstorm systems powering high altitude discharges show the important role of intracloud (IC) lightning in sprite generation as seen by the first simultaneous observations of IC activity, sprite activity and broadband, electromagnetic radiation in the VLF range. Simulations of sprite ignition suggest that, under certain conditions, energetic electrons in the runaway regime are generated in streamer discharges. Such electrons may be the source of X-and Gamma-rays observed in lightning, thunderstorms and the so-called Terrestrial Gamma-ray Flashes (TGFs) observed from space over thunderstorm regions. Model estimates of sprite perturbations to the global atmospheric electric circuit, trace gas concentrations and atmospheric dynamics suggest significant local perturbations, and possibly significant meso-scale effects, but negligible global effects.

We evaluated daily and monthly statistics of maximum and minimum temperatures and precipitation in an ensemble of 16 regional climate models (RCMs) forced by boundary conditions from reanalysis data for . A high-resolution gridded observational data set for land areas in Europe was used. Skill scores were calculated based on the match of simulated and observed empirical probability density functions. The evaluation for different variables, seasons and regions showed that some models were better/worse than others in an overall sense. It also showed that no model that was best/worst in all variables, seasons or regions. Biases in daily precipitation were most pronounced in the wettest part of the probability distribution where the RCMs tended to overestimate precipitation compared to observations. We also applied the skill scores as weights used to calculate weighted ensemble means of the variables. We found that weighted ensemble means were slightly better in comparison to observations than corresponding unweighted ensemble means for most seasons, regions and variables. A number of sensitivity tests showed that the weights were highly sensitive to the choice of skill score metric and data sets involved in the comparison.

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Freshwater runoff to fjords with marine-terminating glaciers along the Greenland Ice Sheet margin has an impact on fjord circulation and potentially ice sheet mass balance through increasing heat transport to the glacier front. Here, the authors use the high-resolution (5.5 km) HIRHAM5 regional climate model, allowing high detail in topography and surface types, to estimate freshwater input to Godthåbsfjord in southwest Greenland. Model output is compared to hydrometeorological observations and, while simulated daily variability in temperature and downwelling radiation shows high correlation with observations (typically >0.9), there are biases that impact the results. In particular, overestimated albedo leads to underestimation of melt and runoff at low elevations. In the model simulation (1991–2012), the ice sheet experiences increasing energy input from the surface turbulent heat flux (up to elevations of 2000 m) and shortwave radiation (at all elevations). Southerly wind anomalies and declining cloudiness due to an increase in atmospheric pressure over north Greenland contribute to increased summer melt. This results in declining surface mass balance (SMB), increasing surface runoff, and upward shift of the equilibrium line altitude. SMB is reconstructed back to 1890 though regression between simulated SMB and observed temperature and precipitation, with added uncertainty in the period 1890–1952 because of possible inhomogeneity in the precipitation record. SMB as low as in recent years appears to have occurred before, most notably around 1930, 1950, and 1960. While previous low SMBs were mainly caused by low accumulation, those around 1930 and in the 2000s are mainly due to warming.

The influence of uncertainties in gridded observational reference data on regional climate model (RCM) evaluation is quantified on a pan‐European scale. Three different reference data sets are considered: the coarse‐resolved E‐OBS data set, a compilation of regional high‐resolution gridded products (HR) and the European‐scale MESAN reanalysis. Five high‐resolution ERA‐Interim‐driven RCM experiments of the EURO‐CORDEX initiative are evaluated against each of these references over eight European sub‐regions and considering a range of performance metrics for mean daily temperature and daily precipitation. The spatial scale of the evaluation is 0.22°, that is, the grid spacing of the coarsest data set in the exercise (E‐OBS). While the three reference grids agree on the overall mean climatology, differences can be pronounced over individual regions. These differences partly translate into RCM evaluation uncertainty. For most cases observational uncertainty is smaller than RCM uncertainty. Nevertheless, for individual sub‐regions and performance metrics observational uncertainty can dominate. This is especially true for precipitation and for metrics targeting the wet‐day frequency, the pattern correlation and the distributional similarity. In some cases the spatially averaged mean bias can also be considerably affected. An illustrative ranking exercise highlights the overall effect of observational uncertainty on RCM ranking. Over individual sub‐domains, the choice of a specific reference can modify RCM ranks by up to four levels (out of five RCMs). For most cases, however, RCM ranks are stable irrespective of the reference. These results provide a twofold picture: model uncertainty dominates for most regions and for most performance metrics considered, and observational uncertainty plays a minor role. For individual cases, however, observational uncertainty can be pronounced and needs to be definitely taken into account. Results can, to some extent, also depend on the treatment of precipitation undercatch in the observational reference.