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This work evaluates the daily precipitation and mean temperature of eight CORDEX‐EUR11 ERA‐Interim‐driven simulations of EURO‐CORDEX over the Iberian Peninsula (IP) for the period 1989–2008. To this aim, three observational data sets (Iberia01, E‐OBS‐v19e, and MESAN‐0.11) were considered as reference and compared with the models by means of several indices reflecting the mean and extreme regimes over the IP. For precipitation the Lamb weather types were considered to identify synoptic conditions related with higher observational uncertainty. RCMs are able to reproduce the spatial pattern and the variability observed in the IP. However, there is a higher agreement between models and observations for mean temperature than for precipitation, decreasing when extremes are analyzed. For the observational uncertainty analysis, also extreme daily temperatures were considered to obtain a wider picture of this topic. A higher dependence on the observational data set has been found for precipitation than for temperature. This uncertainty is particularly significant when the 50‐year return value is considered for which the observational uncertainty doubles the model uncertainty. Only the wet‐day frequency presents values lower than 0.5 for all seasons, with most of the rest of values reflecting a similar contribution of both components to the uncertainty. In the case of temperatures, the main contribution of the observations has been found when the lower (MAE01) and upper (MAE99) extremes are considered, with values lower than 0.5. For precipitation the observational uncertainty increases when synoptic patterns affecting the Mediterranean Basin are considered, reflecting the difficulty to properly capture the Mediterranean precipitation regimes. Key Points: EURO‐CORDEX verication over Iberian PeninsulaObservational uncertainty analysis of the evaluation of the EURO‐CORDEX RCMsSynoptic patterns and observational uncertainty of precipitation regimes [ABSTRACT FROM AUTHOR]

Soil moisture is one of the most important variables of the climate system as it constrains evapotranspiration, affecting the surface energy and water balance. It is of particular importance over transition regions between humid and dry climates as lower evapotranspiration rates lead to higher surface air temperatures thought strong soil moisture‐temperature coupling. A new and more extensive evaluation of the surface energy balance, for the Africa Coordinated Regional Downscaling Experiment, is performed for hindcast simulations, where different Regional Climate Models are driven by ERA‐Interim reanalysis (1990–2008). A new validation is carried out, with Regional Climate Models displaying a good agreement with observations. However, large biases are found over the Sahel and southern Africa for precipitation. All models tend to underestimate maximum temperature, whereas minimum temperature is overestimated for large regions. A multimodel mean ensemble is found to outperform individual models in almost all situations, representing the best estimate of present climate. The seasonal coupling strength as determined by a correlation between latent and sensible heat fluxes indicates Sahel and western Africa as strong coupling for boreal and austral summer, respectively. Also, the strong coupling areas match the transition regions identified by the intermediate values of the evaporative fraction, varying in the spatial extent. Additionally, a new soil moisture‐temperature coupling metric is introduced, which highlights the regions where higher temperatures are influenced by evaporative stress. Therefore, this metric relates extreme daily maximum temperature with latent heat flux and is computed for monthly time scales, incorporating information from the recent past. Key Points: Models are able to capture the main climate patterns within Africa, with some deviations for specific regionsAfrica's transition regions between humid and dry environments are identified as strong soil moisture‐temperature couplingA new soil moisture-temperature coupling metric is introduced allowing the detection of extreme phenomena [ABSTRACT FROM AUTHOR]