This study evaluates the impact of assimilating precipitable water vapor (PWV) within an observing system simulation experiment (OSSE) framework to improve forecasts of monsoonal mesoscale convective systems (MCSs) in Arizona. Two contrasting case studies differing in convective forcing, longevity, intensity, and coverage are analyzed using a 40-member ensemble of 1.8-km resolution Weather Research and Forecasting (WRF) convective-permitting model (CPM) simulations including the Data Assimilation Research Testbed (DART) system. Synthetic PWV data are derived from a nature run (NR) and bias corrected using real GPS-derived PWV observations from a campaign during the North American monsoon (NAM) season 2021. These synthetic PWV are assimilated in an inferior model simulation called the control run (CR) to avoid the identical twin problem. Horizontal GPS station spacing experiments (e.g., superobbed, 50 km, 100 km, and 200 km) are conducted to identify configurations that maximize forecast skills. Assimilating the synthetic PWV reduces mean errors (similar to 2 mm) and dry bias during the first 4-6 hr of the predictions using analyses improved with PWV data assimilation. The 100-km GPS network optimally captures convective precipitation patterns, outperforming coarser (200-km) and finer (50-km) grids due to an improved representation of moisture and winds afforded by PWV data assimilation at the appropriate scales. Topography strongly influences moisture distribution, with elevation-dependent biases, overestimation in low elevations (0-500 m), underestimation in midelevations (500-2,000 m), and systematic high-elevation (>2,000 m) biases due to vertically integrated PWV constraints. This study provides actionable insights for optimizing GPS network design and improving convective-scale modeling in arid/semiarid regions.