The Mediterranean region is a climate change hotspot, especially concerning issues of hydrological planning and urban water supply systems. In this context, the Jucar River Basin (Spain) presents an increase of frequency, intensity and duration of extreme meteorological phenomena, such as torrential rains, droughts or heat waves, which directly affect the quantity and quality of raw water available for drinking. This paper aims to analyze the effects of climate change on the raw water quality of the Jucar River Basin District, which mainly supplies the city of Valencia and its metropolitan area, in order to adapt drinking water treatments to new conditions and opportunities. For this purpose, we used observed data of water quality parameters from four stations and climate drivers from seven Earth system models of the latest Coupled Model Intercomparison Project—Phase 6. To model water quality (turbidity and conductivity) in the past and future scenarios, this study employs a backward stepwise regression taking into account daily values of mean temperature, maximum temperature, total rainfall and minimum and maximum relative humidity. Results showed that the model performance of the water quality simulation is more adequate for short moving-average windows (about 2–7 days) for turbidity and longer windows (about 30–60 days) for conductivity. Concerning the future scenarios, the most significant change was found in the projected increase of conductivity for the station of the Júcar river, between 4 and 11% by 2100, respectively, under the medium (SSP2–4.5) and pessimistic (SSP5–8.5) emission scenarios. The joint use of these types of management and monitoring tools may help the managers in charge of carrying out the different water treatments needed to apply a better plan to raw water and may help them identify future threats and investment needs to adapt the urban water supply systems to the changing conditions of raw water, such as turbidity or conductivity, as a consequence of climate change.

Dynamical climate models present an initialization problem due to the poor availability of deep oceanic data, which is required for the model assimilation process. In this sense, teleconnection indices, defined from spatial and temporal patterns of climatic variables, are conceived as useful tools to complement them. In this work, the near‐term climate predictability of 35 temperature and 36 precipitation time series of three cities (Barcelona, Bristol and Lisbon) was analysed using two approaches: (a) a statistical–dynamical combination of self‐predictable teleconnection indices and long‐term climate projections on a local scale and (b) dynamical model outputs obtained from drift‐corrected decadal experiments. Fourier and wavelet analyses were used to assess the predictability of seven teleconnection indices thanks to a cross‐validation process (with differentiated training and validation periods). The standardized absolute error of teleconnection‐based prediction was compared with that obtained from a (9) multi‐model ensemble based on the Coupled Model Intercomparison Project Phase 5. Results showed that decadal predictions at horizons between 20 and 30 years are adequate for temperature and precipitation if a teleconnection‐based approach is used, while temperature is better predicted from a 5‐year horizon using drift‐corrected dynamical outputs.

RESCCUE is an H2020 research project that aims to help cities around the world to become more resilient to physical, social, and economic challenges, using the water sector as the central point of the approach. Since 2016, RESCCUE has been developing methodologies and tools to support cities increase their resilience. The three RESCCUE cities, Barcelona, Bristol, and Lisbon, have become a testing platform for the cutting-edge technologies developed in RESCCUE but these are also ready to be deployed to different types of cities, with different climate change pressures. This paper presents some of the main outputs generated by RESCCUE. From climate change scenarios to dissemination tools, and from sectorial models to Resilience Action Plans (RAPs), the outputs that have been produced are very diverse, but special focus is put on the urban water cycle and urban floods. All the project results have a common goal: to increase the resilience of cities from around the world, by offering the methodologies and tools so anyone can take advantage of using them and replicate the RESCCUE results.

SICMA-Canarias is an innovative and open-access platform that enables the consultation of current, past, and future climatic conditions in the Canary Islands with a unique resolution of 100 metres by 100 metres. Developed within the framework of the European ARSINOE project, SICMA-Canarias combines advanced climatic data with predictive models extending up to the year 2100, covering key variables such as temperature, precipitation, water balance, heatwaves, and more. This tool is essential for the sustainable management of strategic sectors such as water, agriculture, and natural environment conservation, adapting to the microclimatic singularities of each island.

As climate change is advancing at a quickened pace, the need for a better understanding of local climate change impacts is increasing, especially since the new generation of general circulation models (CMIP6) displays a (statistically nonsignificant) higher global sensitivity than previous climate projections. Within the presented study, two CMIP6 general circulation models are statistically and dynamically downscaled for the Salzburg and South Aegean region to investigate local future climate states under two different shared-socioeconomic pathways until 2100. Comparisons of both methods have been performed previously, however, within this study the innovation lies in the detailed investigation of two regions representing different climatic conditions, geography, and the availability of observational data. Furthermore, the dynamical downscaling is performed down to convection permitting scale, therefore providing information on a 4 km² grid and enabling the representation of local features, as is the case when training a statistical model with observations. The chosen set-up enables us to ask the following questions (i) how do the two methodologies compare for the past period (1981–2010) against high-resolution reanalysis data, (ii) does their skill depend on the region investigated (South Aegean/Salzburg) and on the spatial resolution of the dynamical downscaled data, (iii) how do future conditions coincide/differ between the applied methods and regions?. The results display that, if observation data is available, temperature is better simulated for the past period using statistical downscaling, while precipitation patterns are more realistically met by the dynamical models. Concerning future climate change, the impact is more severe according to the dynamical models in the South Aegean region, while for Salzburg the statistical models display the stronger change, indicating the importance and uncertainty of the chosen downscaling method on localized results.