Unraveling the spatiotemporal dynamics of relative humidity in major Saudi Arabian cities: A synergy of climate modeling, regression analysis, and wavelet coherence

In the rapidly changing climate of arid desert regions, evaluating the comprehensive characteristics of humidity levels is crucial for agricultural, urban, and infrastructural planning, as well as for minimizing potential public health impacts. We investigated variability and trends of humidity levels in major Saudi Arabian cities during 1982-2022, focusing on the influence of meteorological factors such as average, maximum and minimum temperature, rainfall, and windspeed. Employing the Probability Density Function and descriptive statistics, variability of climatic factors was analyzed. The Mann-Kendall Test (MKT) and Innovative Trend Analysis (ITA) were employed to identify monthly and annual trends. The magnitude and changing patterns were determined by calculating Sen's Slope and ITA slope. Findings of the MKT and ITA showed similar trends in humidity levels across all the cities. ITA result revealed that humidity in Riyadh and Taif decreased at a rate of 0.012% and 0.016% per year, respectively, while increased in Jeddah, Makkah, and Madinah at a 0.05 confidence level. The influence of climatic factors on humidity was assessed using Pearson's correlation coefficients, multiple regression model, and wavelet transform coherence (WTC) for each city, pinpointing temperature as the key driver of humidity variability. The dominance of temperature features was corroborated by strong power spectrums in the WTC across various time periods and scales. The in-depth analysis of humidity dynamics in this study provides critical insights for the development of climate-resilient infrastructure and formulation of public health strategies in Saudi Arabian cities.