Here we present the first multi-model ensemble of regional climate simulations at kilometer-scale horizontal grid spacing over a decade long period. A total of 23 simulations run with a horizontal grid spacing of $$\sim $$∼ 3 km, driven by ERA-Interim reanalysis, and performed by 22 European research groups are analysed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution ( $$\sim $$∼ 12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons. The results show that kilometer-scale models produce a more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. On average, the multi-model mean shows a reduction of bias from $$\sim \,$$∼ −40% at 12 km to $$\sim \,$$∼ −3% at 3 km for heavy hourly precipitation in summer. Furthermore, the uncertainty ranges i.e. the variability between the models for wet hour frequency is reduced by half with the use of kilometer-scale models. Although differences between the model simulations at the kilometer-scale and observations still exist, it is evident that these simulations are superior to the coarse-resolution RCM simulations in the representing precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales.

The Land Use and Climate Across Scales Flagship Pilot Study (LUCAS FPS) is a coordinated community effort to improve the integration of land use change (LUC) in regional climate models (RCMs) and to quantify the biogeophysical effects of LUC on local to regional climate in Europe. In the first phase of LUCAS, nine RCMs are used to explore the biogeophysical impacts of re-/afforestation over Europe: two idealized experiments representing respectively a non-forested and a maximally forested Europe are compared in order to quantify spatial and temporal variations in the regional climate sensitivity to forestation. We find some robust features in the simulated response to forestation. In particular, all models indicate a year-round decrease in surface albedo, which is most pronounced in winter and spring at high latitudes. This results in a winter warming effect, with values ranging from +0.2 to +1 K on average over Scandinavia depending on models. However, there are also a number of strongly diverging responses. For instance, there is no agreement on the sign of temperature changes in summer with some RCMs predicting a widespread cooling from forestation (well below −2 K in most regions), a widespread warming (around +2 K or above in most regions) or a mixed response. A large part of the inter-model spread is attributed to the representation of land processes. In particular, differences in the partitioning of sensible and latent heat are identified as a key source of uncertainty in summer. Atmospheric processes, such as changes in incoming radiation due to cloud cover feedbacks, also influence the simulated response in most seasons. In conclusion, the multi-model approach we use here has the potential to deliver more robust and reliable information to stakeholders involved in land use planning, as compared to results based on single models. However, given the contradictory responses identified, our results also show that there are still fundamental uncertainties that need to be tackled to better anticipate the possible intended or unintended consequences of LUC on regional climates.

There is an ongoing debate in the climate community about the benefits of convection-permitting models that explicitly resolve convection and other thermodynamical processes. An increasing number of studies show improvements in Regional Climate Model (RCM) performances when the grid spacing is increased to 1-km scale. Up until now, such studies have revealed that convection-permitting models confer significant advantages in representing orographic regions, producing high-order statistics, predicting events with small temporal and spatial scales, and representing convective organization. The focus of this work is on the analysis of summer precipitation over the Alpine space. More specifically, the driving data are downscaled using the RCM COSMO-CLM first at an intermediate resolution (12 km) over the European Domain of Coordinated Downscaling Experiment (EURO-CORDEX domain). Then, a further downscaling at 3 km, nested into the previous one, is performed over the Alpine domain to exploit the results over a complex orography context. Experiments of evaluation, historical and far future under the Intergovernmental Panel on Climate Change (IPCC) RCP8.5 scenario have been considered. Indices as mean precipitation, frequency, intensity, and heavy precipitation are employed in daily and hourly analyses. The results, observed from the analysis of 10 year-long simulations, provide preliminary indications, highlighting significant differences of the convection permitting simulations with respect to the driving one, especially at an hourly time scale. Moreover, future projections suggest that the convection permitting simulation refines and enhances the projected patterns, compared with the coarser resolution.

This work presents a new dataset for recent climate developed within the Highlander project by dynamically downscaling ERA5 reanalysis, originally available at ≃31 km horizontal resolution, to ≃2.2 km resolution (i.e., convection permitting scale). Dynamical downscaling was conducted through the COSMO Regional Climate Model (RCM). The temporal resolution of output is hourly (like for ERA5). Runs cover the whole Italian territory (and neighboring areas according to the necessary computation boundary) to provide a very detailed (in terms of space–time resolution) and comprehensive (in terms of meteorological fields) dataset of climatological data for at least the last 30 years (01/1989-12/2020). These types of datasets can be used for (applied) research and downstream services (e.g., for decision support systems).

This paper introduces VHR-PRO_IT (Very High-Resolution PROjections for ITaly), an open access hourly climate projection with a resolution of ≃2.2 km (i.e., Convection Permitting Scale) up to 2050, covering the Italian peninsula and some neighbouring areas. VHR-PRO_IT is produced within the Highlander project (https://highlanderproject.eu/) by dynamically downscaling the Italy8km-CM climate projection (spatial resolution ≃8 km; output frequency = 6 h; driven CMIP5 GCM = CMCC-CM) with the Regional Climate Model COSMO-CLM under the IPCC RCP4.5 and RCP8.5 scenarios. It covers the 60-year period 1989–2050. VHR-PRO_IT is intended for research purposes in the field of climate studies. For example, it may be included in the ongoing activities to clarify the added value of running climate simulation at the convection-permitting scale.