This study investigates future changes of temperature, precipitation, and associated extreme events in the MENA region using Regional Climate Model ALADIN-Climate over the CORDEX-MENA domain. Model capabilities to reproduce key observed regional climate features are first assessed, including heat waves, drought and high precipitation extremes. Projected changes indicate the intensification of heat waves number, duration and magnitude, and contrasted precipitation changes. A drying is projected in the north-west and moistening in the north-east along the Mediterranean side of the region. Projected regional warming is found at the rate of about 0.2 °C/decade to 0.5 °C/decade over land depending on the scenario. Drought is expected to increase in the northern half of the region independently from the index used, but with a higher rate in the case of the index accounting for both the effect of precipitation and temperature changes. ALADIN-Climate results corroborate previous studies projecting the MENA region to host global hot spots for drought in the late twenty-first century.

Internal variability, multiple emission scenarios, and different model responses to anthropogenic forcing are ultimately behind a wide range of uncertainties that arise in climate change projections. Model weighting approaches are generally used to reduce the uncertainty related to the choice of the climate model. This study compares three multi-model combination approaches: a simple arithmetic mean and two recently developed weighting-based alternatives. One method takes into account models’ performance only and the other accounts for models’ performance and independence. The effect of these three multi-model approaches is assessed for projected changes of mean precipitation and temperature as well as four extreme indices over northern Morocco. We analyze different widely used high-resolution ensembles issued from statistical (NEXGDDP) and dynamical (Euro-CORDEX and bias-adjusted Euro-CORDEX) downscaling. For the latter, we also investigate the potential added value that bias adjustment may have over the raw dynamical simulations. Results show that model weighting can significantly reduce the spread of the future projections increasing their reliability. Nearly all model ensembles project a significant warming over the studied region (more intense inland than near the coasts), together with longer and more severe dry periods. In most cases, the different weighting methods lead to almost identical spatial patterns of climate change, indicating that the uncertainty due to the choice of multi-model combination strategy is nearly negligible.