In recent years, rainfall in the Jazan region of southwest Saudi Arabia has significantly increased, setting new records for monthly and daily rainfall in 2024 and leading to natural disasters. The distribution of monthly rainfall in Jazan and its variations over recent decades have not been analyzed yet. This study examines the changes in seasonal rainfall patterns in the Jazan region utilizing observational and reanalysis datasets from 1978 to 2024. The rescaled adjusted partial sums technique is used to detect breaks in the rainfall time series, while statistical methods are applied to analyze rainfall extremes and their trends. The average annual rainfall for the period 1978–2024 is 149.4 mm, which has increased from 131.9 mm during the earlier decades (1978–2000) to 166.2 mm in recent decades (2001–2024), reflecting an increase of 34.3 mm. The annual rainfall has been increasing significantly at a rate of 92.9 mm/decade in recent decades, compared to 74.3 mm/decade in the previous decades. There has been a marked shift in the peak rainfall season from autumn to summer, in particular moving from October to August in recent decades. The highest monthly rainfall recorded in August, reached 54.9 mm in recent decades, compared to just 15.4 mm in earlier decades. In contrast, the peak rainfall in October was 19.9 mm in previous decades, which decreased to 18.7 mm in recent decades. Notably, August 2024 marked a record-breaking rainfall of 414.8 mm, surpassing the previous high of 157.5 mm set in October 1997. These data show clear evidence of the changing climate in the region. Moreover, the number of heavy rainfall days has risen, with a total of 608 wet days documented throughout the entire period, alongside a significant increase in light, heavy, and extremely heavy rainfall days in recent decades compared to earlier ones. Hence, the region has seen a rise in heavy to extremely heavy rainfall days, including a daily record of 113.7 mm on 23 August 2024, compared to 90.0 mm on 22 October 1997. Additionally, there has been a rise in the maximum consecutive 5-day rainfall compared to the maximum 1-day rainfall. Overall, these findings show substantial changes in rainfall patterns in the Jazan region, suggesting notable climatic shifts that warrant further investigation using the automatic weather stations, radar and satellite data, as well as climate model simulations.

Atmospheric fine particles (PM_2.5) pose significant health risks by penetrating deep into the lungs and causing respiratory and cardiovascular issues. In Saudi Arabia, high PM_2.5 levels are driven by its geographic location and extreme climate. Therefore, analysis of PM_2.5 spatiotemporal variability is crucial for understanding its causes, impacts, and effective management. This study analyzed MERRA-2 reanalysis PM_2.5 data for 23 years (2001–2023). MERRA-2 data were validated with in situ observations in terms of several statistical metrics, including RMSE, FAC2, MAE, and Correlation Coefficient. The results revealed a significant spatial variation in PM_2.5 levels, with higher concentrations observed in the eastern and southeastern regions and lower concentrations observed in the western and northwestern regions, a trend confirmed by ground-level observations. Employing the robust Theil–Sen technique, temporal trends in PM_2.5 concentrations indicated an overall decreasing trend over the study period. At most sites, PM_2.5 levels increased until 2010 and then started decreasing, probably due to government interventions for reducing emissions, combating desertification, and enhancing tree plantations. Non-linear modeling provided a more accurate representation of complex trends compared to simple linear models. The findings underscore the need for continued national and regional efforts to mitigate PM_2.5 pollution by addressing its emission sources.

Urban heat islands (UHIs) intensify thermal stress in cities, particularly in arid and semi-arid regions undergoing rapid urban expansion. The main objectives of this study are to quantify and compare UHI intensity in six major Saudi Arabian cities (Dammam, Makkah, Madinah, Jeddah, Riyadh, and Abha) representing diverse climatic zones and to examine how UHI patterns vary between urban, suburban, and rural zones over a 30-year period. Understanding the magnitude and spatial variability of UHIs across different climatic settings is crucial for developing effective urban planning and climate adaptation strategies in Saudi Arabia’s rapidly expanding cities. Except for Abha, these cities are the five most populous cities in the Kingdom. Each city was categorized into urban (>1500 people km⁻²), suburban (300–1500 people km⁻²), and rural (<300 people km⁻²) zones using high-resolution population density data. Two independent temperature datasets (ERA5-land and CHIRTS-ERA5) were analyzed for the years 1994, 2004, 2014, and 2024. Both datasets revealed consistent spatial patterns and a general warming trend across all zones and cities over the 30-year period. The UHI effect was most pronounced for minimum temperatures, with urban zones warmer than rural zones by 0.85 °C (ERA5-land) and 1.10 °C (CHIRTS-ERA5), likely due to greater heat retention and slower cooling rates in built-up areas. Mean temperature differences were smaller but still indicated positive UHI. Conversely, both datasets exhibited a reversed UHI pattern for maximum temperatures, with rural zones warmer than urban zones by 1.73 °C (ERA5-land) and 1.52 °C (CHIRTS-ERA5). This reversed pattern is attributed to the surrounding desert landscapes with minimal vegetation, indicated by low normalized difference vegetation index (NDVI), while urban areas have increasingly benefited from greening and landscaping initiatives. City-level analysis showed the strongest reversed UHI in maximum temperatures in Abha, while Jeddah exhibited the weakest. These findings highlight the need for localized urban planning strategies, particularly the expansion of vegetation cover and sustainable land use, to mitigate extreme thermal conditions in Saudi Arabia.

The increasing occurrence of extreme rainfall events often leads to flash floods, infrastructure damage, loss of human life, and significant economic impacts. There is a pressing need for data-driven assessments and the application of robust analytical approaches to better understand these changes. Analyzing ground-level daily rainfall data from 1985 to 2023 from 26 monitoring stations, this study first employs the Mann–Kendall test using robust statistics including minimum, median, various quartiles, and maximum rainfall values for detecting long-term trends across Saudi Arabia. Next, the k-means clustering technique is applied to characterize the annual rainfall cycles across different regions of the country. Finally, the Peaks Over Threshold (POT) approach within Extreme Value Theory (EVT) is employed to identify site-specific thresholds for extreme rainfall using the Generalized Pareto Distribution (GPD). This automated, data-driven method offers a more objective alternative to the commonly used ad hoc percentile-based threshold selection, thereby enhancing the rigour and reproducibility of extreme rainfall analysis. Local specific thresholds were computed ranging from about 16 to 47 mm from Arar and Jazan, respectively. These thresholds were then used to calculate the frequency and intensity of extreme rainfall events. The fitted GPD parameters were further used to estimate return levels (RLs) for different return periods (2-, 5-, 10-, 20-, 50-, and 100-year) into the future. The results underscore considerable spatial variability in extreme rainfall behaviour across Saudi Arabia, with a higher likelihood of intense and infrequent precipitation events in the coming decades.