Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semi-arid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal drought. Droughts here are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region’s hydrologic cycle. An extensive season-ahead drought prediction model is developed to help bolster existing capacity of stakeholders to plan for and mitigate the deleterious impacts of this hydrologic extreme. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to eleven potential predictors to produce an ensemble forecast of January-March precipitation. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. Extending the lead time and spatially disaggregating precipitation predictions to the local level may further assist regional stakeholders and policymakers preparing for drought.