A simple—yet powerful—indicator for monitoring agricultural drought is the Water Requirement Satisfaction Index (WRSI). In data-sparse, food-insecure areas, the WRSI is used to guide billions of dollars of aid every year. The WRSI uses precipitation (PPT) and reference evapotranspiration (RefET) data to estimate water availability relative to water demand experienced over the course of a growing season. If the season is in progress, to-date conditions can be combined with climatological averages to provide insight into potential end-of-season (EOS) crop performance. However, if the average is misrepresented, these forecasts can hinder early warning and delay precious humanitarian aid. While many agencies use arithmetic average climatologies as proxies for “average conditions,” little published research evaluates their effectiveness in crop-water balance models. Here, we use WRSI hindcasts of three African regions’ growing seasons, from 1981-2019, to assess the adequacy of the arithmetic mean climatological forecast—the Extended WRSI. We find the Extended WRSI is positively biased, overestimating the actual EOS WRSI by 2-23% in east, west, and southern Africa. The presented alternative combines to-date conditions with data from previous seasons to produce a series of historically realistic conclusions to the current season. The mean of these scenarios is the WRSI Outlook. Put in comparison to the Extended WRSI, which creates a single forecast scenario using average inputs that are not co-varying, the WRSI Outlook employs an ensemble of scenarios, which more adequately capture the historical distribution of distribution of rainfall events, as well as the covariability between climate variables. More specifically, the impact of dry spells in individual years is included in the WRSI Outlook, in a way that is smoothed over in the Extended WRSI. We find the WRSI Outlook has a near-zero bias score and generally has a lower RMSE. In total, this paper highlights the inadequacies of the arithmetic mean climatological forecast, and presents a less-biased, and more accurate, scenario-based approach. To this end, the WRSI Outlook can improve our ability to identify agricultural drought and the concomitant need for humanitarian aid.