Climate change effects on subdaily rainfall (from 5 min to a few hours) can hardly be measured in mid‐latitude climates due to the high natural variability of the precipitation patterns and their effects on local topography. The goal of this study was to obtain change projections of intensity–duration–frequency (IDF) curves, for up to 2‐h precipitation events, comparing two approaches that use the daily outputs of the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model projections: (a) direct scaling of the expected probable precipitation, from 2‐year to 500‐year return periods of daily rainfall and (b) a new semi‐stochastic approach, built by combining the physically forced outputs of climate models (on a daily scale) and stochastic simulation given by the probability distribution of a concentration index (n‐index) for individual rainfall events (on a subdaily scale). The approaches were applied to a set of 27 stations located around Barcelona, Spain, including a long reference series (with 5‐min rainfall records since 1927), representing the highly variable Mediterranean climate. The validation process showed a systematic error (bias) generally smaller than 10%, especially for rainfall extreme events with durations of less than 2 h. The concentration n‐index and IDF curves were projected by 10 downscaled CMIP5 climate models under 2 emission scenarios (RCP4.5 and RCP8.5), obtaining a consensual increase in both relative concentration and absolute intensities in Barcelona. Ensemble projection of rainfall concentration (n‐index) showed an increase up to 10% by 2071–2100 and about 20% (15%–30% range) for maximum intensities of 2‐year to 500‐year return periods. Results provide robustness in decision‐making regarding the design of stormwater management infrastructure at a local scale.

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.

Rainfall regimes are experiencing variations due to climate change, and these variations are adequately simulated by Earth System Models at a daily scale for most regions. However, there are not enough raw outputs to study extreme and sub-daily precipitation patterns on a local scale. To address this challenge, Monjo developed the n-index by characterizing the intensity and concentration of precipitation based on mono-fractal theory. In this study, we explore the use of a multi-fractal approach to establish a more accurate method of time scaling useful to study extreme precipitation events at a finer temporal resolution. This study was carried out on the reference station of Barcelona (Spain) and its surroundings in order to be representative of the Mediterranean climate. For return periods between 2 and 50 years, two variables were analyzed: the n-index and the reference intensity $I_0$. Moreover, a new parameter, the so-called “breaking point”, was designed here to describe the reference intensity $I_0$, which is predominant for low time ranges. The results showed that both parameters are dependent on the time steps and the return period, and the scores confirmed the validity of our approach. Finally, the n-index was projected under downscaled CMIP6 climate scenarios by 2100, showing a sustained increase of up to +10%.

It is widely known from energy balances that global oceans play a fundamental role in atmospheric seasonal anomalies via coupling mechanisms. However, numerical weather prediction models still have limitations in long-term forecasting due to their nonlinear sensitivity to initial deep oceanic conditions. As the Mediterranean climate has highly unpredictable seasonal variability, we designed a complementary method by supposing that 1) delayed teleconnection patterns provide information about ocean–atmosphere coupling on subseasonal time scales through the lens of 2) partially predictable quasi-periodic oscillations since 3) forecast signals can be extracted by smoothing noise in a continuous lead-time horizon. To validate these hypotheses, the subseasonal predictability of temperature and precipitation was analyzed at 11 reference stations in the Mediterranean area in the 1993–2021 period. The novel method, presented here, consists of combining lag-correlated teleconnections (15 indices) with self-predictability techniques of residual quasi-oscillation based on wavelet (cyclic) and autoregressive integrated moving average (ARIMA) (linear) analyses. The prediction skill of this teleconnection–wavelet–ARIMA (TeWA) combination was cross-validated and compared to that of the ECMWF’s Seasonal Forecast System 5 (SEAS5)–ECMWF model (3 months ahead). Results show that the proposed TeWA approach improves the predictability of first-month temperature and precipitation anomalies by 50%–70% compared with the forecast of SEAS5. On a moving-averaged daily scale, the optimum prediction window is 30 days for temperature and 16 days for precipitation. The predictable ranges are consistent with atmospheric bridges in teleconnection patterns [e.g., Upper-Level Mediterranean Oscillation (ULMO)] and are reflected by spatial correlation with sea surface temperature (SST). Our results suggest that combinations of the TeWA approach and numerical models could boost new research lines in subseasonal-to-seasonal forecasting. Significance Statement: The Mediterranean climate presents a high natural variability that makes skillful seasonal forecasts very difficult to achieve. We propose to complement the current forecasting methods with a statistical approach that combines two conceptual models: First, climate anomalies (cold/warm or dry/wet periods) are considered as smooth waves (with slow changes); and second, atmospheric and oceanic indices perform the role of atmosphere–ocean interactions, which impact Mediterranean climate variability in a delayed way. The key findings are that combining both sides, a better predictability of climate variability is provided, which is an opportunity to improve natural resource management and planning.