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The present study assesses the climate of the Middle East and North Africa (MENA) in detail, focusing on the historical and future warming trends in the region. The assessment incorporates data from observations, reanalyses, and statistically downscaled climate models from the Coupled Model Intercomparison Project Phases 5 and 6. Since the pre‐industrial era, the observed average climate in the MENA has warmed by 1.5°C and is on the brink of exceeding 2°C. The reanalysis data suggest that the regional warming over some MENA sub‐regions is three times faster than the global average. By the end of the 21st century, the Arabian Peninsula is projected to warm to 2.6°C ± 0.57°C and 7.6°C ± 1.53°C under low and high greenhouse gas emission scenarios, respectively. Distinct warming hotspots emerge over the Arabian Peninsula and Algeria in summer and over Mauritania in West Africa and the Elburz Mountains in Iran in winter. The summer hotspot over the Arabian Peninsula has already warmed by more than 2°C and can potentially warm to approximately 9°C under the high‐emission scenario. As global warming progresses to 1.5, 2.0, 3.0, and 4.0°C, the average temperature over the MENA land is projected to increase by 2.3°C ± 0.18°C, 3.0°C ± 0.22°C, 4.6°C ± 0.26°C, and 6.1°C ± 0.31°C, respectively. The 1.5, 2.0, 3.0, and 4.0°C warming levels over the MENA are expected to predate those of the global mean by two or three decades. Natural climate fluctuations also significantly influence the region's warming, contributing to temperature extremes. Plain Language Summary: Warming of the Earth's climate due to anthropogenic influences is expected to continue in the future. The present study examines in detail the actual and projected temperature changes in the Middle East and North Africa (MENA) region from 1850 to the end of this century. Using the most recently available data and models, we show that the region, notably the Arabian Peninsula, is warming significantly faster than the rest of the world. The high warming rate in the central part of the Arabian Peninsula is comparable to that in the Arctic and is two to three times the global average. This finding is concerning because the Arabian Peninsula is already one of the hottest locations on Earth. Additional warming hotspots are Algeria, Mauritania, and the Elburz Mountains in Iran. Rapid warming in the MENA will considerably affect the population and may compromise habitability. Key Points: Middle East and North Africa are warming much faster than the global average, with notable hotspots in the Arabian Peninsula and AlgeriaMiddle East and North Africa will reach 1.5, 2, 3, and 4°C warming levels two to three decades earlier than the global averageBy 2100, high emissions could warm parts of the Arabian Peninsula by 9°C, posing severe environmental and societal challenges [ABSTRACT FROM AUTHOR]

Climate models are useful tools for analyzing historical and projecting future climate conditions. However, the model results tend to differ systematically from observations, particularly for parameters with complex spatial and temporal distributions such as precipitation. A combination of quantile mapping and generalized additive models (GAMs) is presented and proposed as a new method (Q‐GAM) for the bias correction of daily precipitation. Q‐GAM is demonstrated by using data from five European stations with different climate characteristics. For each station, the closest continental grid point of a EURO‐CORDEX climate model was selected for bias correction. A bootstrapping experiment is presented with over 1000 repetitions of randomly splitting the historical period 1981 to 2005 into a calibration and evaluation period. The results for all stations reveal that Q‐GAM is a straightforward, accurate and computationally efficient method for the bias correction of precipitation. In particular, the method improves the frequency of dry days and the total annual rainfall amount. This outcome is robust across stations with varying climate characteristics and also to the choice of calibration and evaluation periods. Similar results are also obtained for other precipitation characteristics such as the 0.9 and 0.95 quantiles. [ABSTRACT FROM AUTHOR]

Precipitation holds significant importance as a climate parameter in various applications, including studies on the impacts of climate change. However, its simulation or projection accuracy is low, primarily due to its high stochasticity. Specifically, climate models often overestimate the frequency of light rainy days while simultaneously underestimating the total amounts of extreme observed precipitation. This phenomenon, known as "drizzle bias", specifically refers to the model's tendency to overestimate the occurrence of light precipitation events. Consequently, even though the overall precipitation totals are generally well represented, there is often a significant bias in the number of rainy days. The present study aims to minimize the drizzle bias in model output by developing and applying two statistical approaches. In the first approach, the number of rainy days is adjusted based on the assumption that the relationship between observed and simulated rainy days remains the same in time (thresholding). In the second, a machine learning method (random forest or RF) is used for the development of a statistical model that describes the relationship between several climate (modelled) variables and the observed number of wet days. The results demonstrate that employing a multivariate approach yields results that are comparable to the conventional thresholding approach when correcting sub-periods with similar climate characteristics. However, the importance of utilizing RF becomes evident when addressing periods exhibiting extreme events, marked by a significantly distinct frequency of rainy days. These disparities are particularly pronounced when considering higher temporal resolutions. Both methods are illustrated on data from three EURO-CORDEX climate models. The two approaches are trained during a calibration period, and they are applied for the selected evaluation period. [ABSTRACT FROM AUTHOR]

Global and regional climate models' prediction accuracy is limited, often with systematic biases between the model output and observed conditions. The present research aims to minimize the biases between the maximum temperature simulated by a global (HadGEM3) and a regional (WRF) climate model for the period of 2006–2014, based on the ERA5 reanalysis data, used as a proxy for observations (calibration period 1981–2000). For the bias correction, a new approach with the TIN‐Copula method (combination of Triangular Irregular Networks‐TINs and Copulas), suitable for gridded datasets is used. The methodology applies TINs to define the grids to which the corrections are made, and copulas for analysing the dependence between the reanalysis and simulated data during the calibration period. The dependence structure is then used for the bias adjustment. Our methodology is applied to the Middle East and North Africa MENA‐CORDEX domain, where extreme heat conditions prevail, which are projected to accelerate during the 21st century. Besides to the entire area, a detailed analysis was performed for six subregions. Our results indicate that the maximum temperature output of both models diverges from the ERA5 reanalysis, especially during summer. In several subregions and seasons, discrepancies are highest for the HadGEM3 model, which has a much coarser resolution than WRF. The effectiveness of the TIN‐Copula method was tested spatially, temporally and based on two relevant climate indices [daily maximum temperature (TXx) and warm spell duration index (WSDI]), showing that it minimizes model biases. [ABSTRACT FROM AUTHOR]

Observation and model-based studies have identified the Mediterranean region as one of the most prominent climate change "hot-spots." Parts of this distinctive region are included in several Coordinated Regional Downscaling Experiment (CORDEX) domains such as those for Europe, Africa, the Mediterranean, and the Middle East/North Africa. In this study, we compile and analyze monthly temperature and precipitation fields derived from regional climate model simulations performed over different CORDEX domains at a spatial resolution of 50 km. This unique multi-model, multi-scenario, and multi-domain "super-ensemble" is used to update projected changes for the Mediterranean region. The statistical robustness and significance of the climate change signal is assessed. By considering information from more than one CORDEX domains, our analysis addresses an additional type of uncertainty that is often neglected and is related to the positioning of the regional climate model domain. CORDEX simulations suggest a general warming by the end of the century (between 1 and 5 °C with respect to the 1986–2005 reference period), which is expected to be strongest during summer (up to 7 °C). A general drying (between 10 and 40%) is also inferred for the Mediterranean. However, the projected precipitation change signal is less significant and less robust. The CORDEX ensemble corroborates the fact that the Mediterranean is already entering the 1.5 °C climate warming era. It is expected to reach 2 °C warming well within two decades, unless strong greenhouse gas concentration reductions are implemented. The southern part of the Mediterranean is expected to be impacted most strongly since the CORDEX ensemble suggests substantial combined warming and drying, particularly for pathways RCP4.5 and RCP8.5. [ABSTRACT FROM AUTHOR]

The objective of this analysis is to provide an up-to-date observation-based assessment of the evolution of temperature extremes in the Middle East–North Africa (MENA) region and evaluate the performance of global climate model simulations of the past four decades. A list of indices of temperature extremes, based on absolute level, threshold, percentile and duration is used, as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We use daily near-surface air temperature (Tmax and Tmin) to derive the indices of extremes for the period 1980–2018 from: (i) re-analyses (ERA-Interim, MERRA-2) and gridded observational data (Berkeley Earth) and (ii) 18 CMIP5 model results combining historical (1950–2005) and scenario runs (2006–2018 under RCP 2.6, RCP 4.5 and RCP 8.5). The CMIP5 results show domain-wide strong, statistically significant warming, while the observation based ones are more spatially variable. The CMIP5 models capture the climatology of the hottest areas in the western parts of northern Africa and the Gulf region with the thewarmest day (TXx) > 46 °C and warmest night (TNx) > 33 °C. For these indices, the observed trends are about 0.3–0.4 °C/decade while they are 0.1–0.2 °C/decade stronger in the CMIP5 results. Overall, the modeled climate warming up to 2018, as reflected in the indices of temperature extremes is confirmed by re-analysis and observational data. [ABSTRACT FROM AUTHOR]

The study of water balance is considered here as a way to assess the performance of regional climate models and examine model uncertainty and as an approach to understanding regional hydrology, especially interactions between atmospheric and hydrological processes. We studied the atmospheric and terrestrial water balance over the Eastern Nile Basin (ENB) region using the weather research and forecasting (WRF) model. The model performance in simulating precipitation and surface air temperature is assessed by comparing the model output with the data from the Global Precipitation Climatology Center dataset for precipitation and from the University of Delaware for temperature. The results show that the simulated and observed values correlate well. In terms of water balance, the study region was found to be a sink for moisture, where the atmospheric convergence is negative during most of the time. Most of the precipitation originates from moisture fluxes from outside the domain, and the contribution of local evapotranspiration to precipitation is limited, with small values for the moisture recycling ratios year-round. The atmospheric moisture content does not show significant monthly or annual variation. The results indicate that the terrestrial water storage varies seasonally, with negative fluxes during most of the year, except June, July, and August, when most of the precipitation occurs. [ABSTRACT FROM AUTHOR]

The Eastern Mediterranean (EM) and the Middle East and North Africa (MENA) are projected to be exposed to extreme climatic conditions in the 21st century, which will likely induce adverse impacts in various sectors. Relevant climate change impact assessments utilise data from climate model projections and process-based impact models or simpler, index-based approaches. In this study, we explore the implied uncertainty from variations of climate change impact-related indices as induced by the modelled climate (WRF regional climate model) from different land surface schemes (Noah, NoahMP, CLM and RUC). The three climate change impact-related indicators examined here are the Radiative Index of Dryness (RID), the Fuel Dryness Index (Fd) and the Water-limited Yield (Yw). Our findings indicate that Noah simulates the highest values for both RID and Fd, while CLM gives the highest estimations for winter wheat Yw. The relative dispersion in the three indices derived by the different land schemes is not negligible, amounting, for the overall geographical domain of 25% for RID and Fd, and 10% for Yw. The dispersion is even larger for specific sub-regions. [ABSTRACT FROM AUTHOR]