The Coordinated Regional Downscaling Experiment (CORDEX) offers a framework for regional climate downscaling experiments over continental‐based domains that overlap; this allows for conducting multimodel analysis. However, a question is raised about the uncertainty due to domain selection. This study aims to evaluate the sensitivity of CORDEX simulations to the choices of domain, downscaling regional climate model (RCM) and driving global model (GCM) over a Middle East study region, including Jordan. Understanding these sensitivities helps assess simulation uncertainties and enhance regional climate change projections. Taylor diagrams and variance decomposition analyses were used to analyse seasonal temperature and precipitation climatologies. The results indicate that the domain contribution to variance is negligible, whereas the choice of GCM and RCM strongly influences simulations. Variance in temperature is significantly impacted by the driving model (GCM), whereas RCM has a higher influence on precipitation, which reflects their large‐scale versus local nature. The results of this study support the development of multidomain ensembles since projections produced by the same GCM–RCM model combination are consistent across different overlapping domains. On the other hand, this approach facilitates the consideration as well as a comprehensive quantification of the uncertainties arising from the utilization of multiple GCMs and RCMs within such an ensemble. This improves the reliability of regional climate information, thus facilitating the development of effective adaptation strategies and mitigation plans.

Wheat, a cornerstone of Jordan’s food security and agricultural economy, has experienced fluctuating production dynamics. Population growth, climate variability, and the gradual integration of advanced farming technologies drive these fluctuations. As the population expands, the demand for wheat naturally escalates, placing increased pressure on local production capabilities. This study aimed to analyze the impact of demographic (population) and climatic parameters of rainfall and maximum and minimum temperature on wheat production in Jordan from 1995 to 2022. Geographic Information Systems (GIS) techniques and statistical analysis methods, specifically utilizing Geographically Weighted Regression (GWR), were used to analyze the relationship between wheat production and population within specific periods; this study revealed an inverse relationship of coefficient of determination with (R² = −0.56, −0.78 and −0.89) for the north, middle, and south regions of Jordan, respectively. A direct relationship between wheat production and rainfall and temperature is explored, especially in the southern areas of Jordan (R² = 0.63 and 0.81), respectively. Generally, rainfall accounted for (32.26%) of the variations in wheat production, while the minimum temperature contributed (30.74%). The effect of maximum temperature was less significant, explaining the variations (13.24%). GWR tests confirmed these climatic factors’ independence, indicating each’s direct impact on wheat production. However, the total decrease in wheat production from 1995 to 2022 comprised approximately 34.19% of production. However, the total reduction in wheat production is shown from 1995 to 2022, approximately 34.19% of production. This study recommends conducting further research to delve deeper into the interplay between population growth, climatic changes, and agricultural practices, exploring the potential for developing sustainable strategies to mitigate the decline in wheat production and ensure food security in Jordan.