Forecast verification is essential for improving weather prediction models but faces challenges with traditionally used metrics. New spatial verification metrics like the Fraction Skill Score (FSS) perform better but are difficult to use in a global domain due to large computational cost. We introduce two new global smoothing methodologies that can be used with smoothing-based metrics in a global domain. We demonstrate their effectiveness with an analysis of global precipitation forecasts.