Climate Network Analysis of Precipitation Regimes from WorldClim Data in Saudi Arabia

Saudi Arabia is shaped by a hydroclimatic gradient, from the hyper-arid Rub’ al-Khali desert to the semi-arid mountains in the southwest. This gradient affects runoff generation, groundwater recharge, and drought risk, yet most studies still summarize rainfall using basic statistics from station data or gridded products. This research applies climate network analysis to identify coherent rainfall regimes across Saudi Arabia. Monthly precipitation climatology (1970-2000) from WorldClim v2.1 at 2.5 arc-minute resolution was extracted for 98,719 land grid cells. Cells were assigned to three macro-zones based on annual precipitation: hyper-arid (less than 75 mm), arid (75-150 mm), and semi-arid (150 mm or more). A stratified sampling scheme selected 7,148 representative nodes while preserving the distribution of annual rainfall. Within each zone, we constructed climate networks by linking nodes with highly similar seasonal cycles. We then computed standard network metrics (degree centrality, betweenness centrality) and identified communities through a modularity-based algorithm. The zonation reproduces the precipitation gradient, and the sampling design captures the range of annual totals. In each zone networks exhibit dense connectivity and two or three communities that align with seasonal regimes and topographic settings. Nodes with high degree centrality mark areas where the seasonal cycle is typical of their zone, whereas nodes with elevated betweenness centrality occur along transition belts and coastal margins that connect distinct regimes. Sensitivity tests across correlation thresholds show that community patterns are robust even as network density and centrality values decline. By converting rainfall data into a network structure, this study provides a framework to describe spatial rainfall behavior. The framework offers practical value for hydrological analysis, water-resource planning, and flood and drought preparedness.