Landslides are important geomorphic events that sculpt river basins by eroding hillslopes and providing sediments to coastal areas. However, landslides are also hazardous events for socio‐ecological systems in river basins causing enormous biodiversity, economic, and social impacts. We propose a probabilistic spatially explicit model for the prediction of landslide patterns based on a maximum entropy principle model (MAXENT). The model inputs are the centers of mass of historical landslides and environmental variables at the basin scale. The model has only three parameters requiring calibration: the threshold for the network extraction, the trade‐off factor between model complexity and accuracy, and the threshold of landslide susceptibility. The calibration on a subset of observations detects the environmental drivers and their relative importance for landslides. We employ the model in the Arno basin, Italy, selected because of its widespread landslide dynamics and the large availability of landslide observations. The model reproduces the size distribution and location of over 27,500 historical landslides for the Arno basin with an accuracy of 86% obtained from the variable‐landslide inference on about 37% of observed landslides. Future landslide patterns are predicted for 17 A1B and A2 rainfall scenarios and for a multimodel ensemble from 2000 to 2100. We show that potential landslide hazard is strongly correlated with variation in the 12 and 48 h rainfall with a return time of 10 years. As the climate gets wetter, the average probability of landslides gets higher which is shown by the landslide size distribution. Hence, the landslide size distribution is a fingerprint of the geomorphic effectiveness of rainfall as a function of climate change. MAXENT is proposed as a parsimonious model for the prediction of landslide patterns with respect to more complex models. The need for very accurately sampled and delineated landslides is lower than for other prediction models. Moreover, the model informs about the drivers of landslides and their relative importance without assumptions on the main triggering factors. This is important to inform monitoring of environmental variables. Our modeling approach can enhance the planning of socio‐ecological systems in river basins by improving the accuracy of landslide prediction in space and time.

The EU Copernicus Climate Change Service (C3S) European Climatic Energy Mixes (ECEM) has produced, in close collaboration with prospective users, a proof-of-concept climate service, or Demonstrator, designed to enable the energy industry and policy makers assess how well different energy supply mixes in Europe will meet demand, over different time horizons (from seasonal to long-term decadal planning), focusing on the role climate has on the mixes. The concept of C3S ECEM, its methodology and some results are presented here. The first part focuses on the construction of reference data sets for climate variables based on the ERA-Interim reanalysis. Subsequently, energy variables were created by transforming the bias-adjusted climate variables using a combination of statistical and physically-based models. A comprehensive set of measured energy supply and demand data was also collected, in order to assess the robustness of the conversion to energy variables. Climate and energy data have been produced both for the historical period (1979–2016) and for future projections (from 1981 to 2100, to also include a past reference period, but focusing on the 30 year period 2035–2065). The skill of current seasonal forecast systems for climate and energy variables has also been assessed. The C3S ECEM project was designed to provide ample opportunities for stakeholders to convey their needs and expectations, and assist in the development of a suitable Demonstrator. This is the tool that collects the output produced by C3S ECEM and presents it in a user-friendly and interactive format, and it therefore constitutes the essence of the C3S ECEM proof-of-concept climate service.

The EU Copernicus Climate Change Service (C3S) has produced an operational climate service, called C3S Energy, designed to enable the energy industry and policymakers to assess the impacts of climate variability and climate change on the energy sector in Europe. The C3S Energy service covers different time horizons, for the past 40 years and the future. It provides time series of electricity demand and supply from wind, solar photovoltaic and hydropower, and can be used for recent trends analysis, seasonal outlooks or the assessment of climate change impacts on energy mixes in the long term. This article introduces this service and the resulting dataset, with a focus on the design and validation of the energy conversion models, based on ENTSO‐E energy data and the ERA5 climate reanalysis. Flexibility and coherence across all countries have been preferred upon models' accuracy. However, the comparison with ENTSO‐E data shows that the models provide plausible energy indicators and, in particular, allow comparing climate variability effects on power demand and generation in a harmonized manner all over Europe.

Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments.