Annual monsoonal rains provide the primary water source for agricultural production in West Africa; thus, the timing of the West African Monsoon (WAM) is directly tied to the livelihoods of subsistence farmers in this region and any progress on the understanding of Sahelian climate would benefit the socioeconomic development of the region. Using the Rainy and Dry Season (RADS) dataset, this study sought to determine the role of remote climate indices on the spatiotemporal variability of WAM dynamics from 1982-2018 using correlation and regression analysis. Specifically, three sea-surface temperature (SST) indices, Ni?o3.4, Dipole Mode Index (DMI), and Equatorial Mode (EM), were used to represent the state of the Pacific, Indian, and Atlantic Oceans, respectively, to determine the influence on the WAM's total wet season precipitation (TWSP), onset, and demise date. Further examining the spatial influence of each SST index, the study region was delineated into three latitudinal zones, the Guinean Coast, Transition, and Sahelian zones, corresponding to the WAM's three phases. Furthermore, a gridded SST analysis was conducted for all global ocean areas to identify additional relationships with WAM dynamics. The Guinean Coast experienced increased TWSP during periods of low SST in the tropical Pacific and high SST in the tropical Atlantic Ocean (and vice versa). The EM represents the best predictor for TWSP, especially along the Guinean Coast. On average, WAM onsets have been occurring earlier in the Sahel and WAM demise has been occurring later in the summer across the study region. During periods of high DMI, the Transition zone experienced delayed rainy seasons. SST in the tropical Indian Ocean could be a potential predictor of WAM onset. SSTs in the westernmost Indian Ocean were found to be strongly correlated to WAM demise in the basin-wide gridded SST analysis. When all three SST indices were evaluated jointly, we found them to account for 21-73% of TWSP variability and 20-55% for both onset and demise variability. Given the accessibility of RADS, this dataset holds value for gauging the onset and demise of monsoonal precipitation in West Africa.
