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Using the gridded monthly Global Precipitation Climatology Centre (GPCC) version 2022 dataset, together with National Centers for Environmental Prediction (NCEP) R1 reanalysis data and Hadley Centre sea surface temperature (SST) data, we conducted a diagnostic analysis of the interannual variability of the leading modes of seasonal rainfall over southern Africa for the period 1950–2020. Although several modes were identified, only the first four modes are analyzed for each season considered (SON, DJF, MAM, and JJA). Because unrotated modes may be physically ambiguous or represent spurious patterns, a varimax orthogonal rotation was applied. This rotation maximizes the variance within localized regions of the domain, thereby enhancing the physical interpretability of the modes. The characteristics and associated climate signals of the four rotated modes are examined in this study. During austral spring (SON), the first mode exhibits a dipole-like structure, with strong positive loadings over most of SAF and weak or negligible loadings over the coastal regions of South Africa and southern Mozambique. This mode appears to be primarily associated with ENSO variability, with a weaker secondary connection to the Indian Ocean Dipole (IOD). The second mode displays a diagonal dipole-like pattern across SAF, resembling a La Niña–type rainfall response. The third mode shows a tripole-like structure, characterized by positive loadings over the core monsoon region of southeastern SAF and negative loadings on either side, and is linked to the Subtropical Indian Ocean Dipole (SIOD). The fourth mode presents a zonal tripole-like pattern, with positive loadings over central-western SAF and negative loadings over northern and southern SAF. All four leading modes are present across the four seasons considered, although their relative contributions and the sign and magnitude of their loadings vary seasonally. The associated SST patterns are consistent with known large-scale circulation anomalies linked to these climate modes. This analysis improves the understanding of the dominant drivers of seasonal rainfall variability over southern Africa and provides a useful framework for interpreting regional climate variability and potential predictability.