Malaria is among the greatest public health threats in Mozambique, with over 10 million cases reported annually since 2018. Although the relationship between seasonal trends in environmental parameters and malaria cases is well established, the role of climate in deviations from the annual cycle is less clear. To investigate this and the potential for leveraging inter-annual climate variability to predict malaria outbreaks, weekly district-level malaria incidence spanning 2010-2017 were processed for a cross-analysis with climate data. An empirical orthogonal function analysis of district-level malaria incidence revealed two dominant spatiotemporal modes that collectively account for 81% of the inter-annual variability of malaria: a mode dominated by variance over the southern half of Mozambique (64%), and another dominated by variance in the northern third of the country (17%). These modes of malaria variability are shown to be closely related to precipitation. Linear regression of global sea surface temperatures onto local precipitation indices over these variance maxima links the leading mode of inter-annual malarial variability to the El Nino-Southern Oscillation, such that La Nina leads to wetter conditions over southern Mozambique and, therefore, higher malaria prevalence. Similar analysis of spatiotemporal patterns of precipitation over a longer time period (1979-2019) indicate that the Subtropical Indian Ocean Dipole is both a strong predictor of regional precipitation and the climatic mechanism underlying the second mode of malarial variability. These results suggest that skillful malaria early warning systems may be developed that leverage quasi-predictable modes of inter-annual climate variability in the tropical oceans. Plain Language Summary Malaria is one of the main public health concerns in Mozambique, with millions of reported cases in the country each year. While malaria has been tied to monthly swings in rainfall and temperature, its relationship to year-to-year changes of the climate is less well known. We identified regions where local malaria cases varied together and found two main patterns: a main hotspot over the southern half of Mozambique, and a second hotspot over the northern third of the country. Rainfall drives both of these hotspots. We then tied these patterns to two natural climate phenomena, the El Nino-Southern Oscillation and the Subtropical Indian Ocean Dipole, both of which impact the climate of the region and help drive malaria prevalence. Our results suggest that it may be possible to take advantage of the predictability of these climate phenomena to improve public health planning both in Mozambique and more broadly.