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.

The extensive use of high-resolution digital elevation data, along with continuous improvements in computing power and geographic information system (GIS) tools. Has driven the development of spatial data processing, management, and spatial interpolation methods. This study aims to construct a high-quality spatial distribution map of thermal comfort in densely populated areas of northwestern Jordan using climate data collected from six meteorological stations between 1991 and 2024, based on the indoor temperature index (IAT). To analyze the spatial variability of climate elements, a digital elevation model (DEM) with a spatial resolution of 30 m was used and resampled to a 0.5-km grid. Spatial interpolation employed inverse distance weighting (IDW), with each grid cell using data from the three nearest meteorological stations. The results showed that areas with higher temperatures inside the villas were clearly concentrated in the summer, especially in the lowlands near the Jordan Valley. Indicating that these areas are more susceptible to thermal stress. The model results also show that it performs well in predicting thermal comfort, with a coefficient of determination (R²) between 0.95 and 0.98 and mean squared error (MSE) between 0.35 and 0.50. which reflects the ability of these models to represent the relationship between climate variables and predict thermal comfort levels with a high degree of accuracy. The results indicate significant spatiotemporal differences in thermal comfort within the study area, with longer durations of heat stress in summer. This highlights the importance of combining geospatial methods with numerical simulations in studying the impacts of climate change and supporting urban planning and climate adaptation strategies.