Australian rainfall is related with numerous key climate predictors namely El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Southern Annular Mode (SAM). Some studies have tried to discover the effects of these climate predictors on rainfall variability of different parts of Australia, particularly Western Australia, Queensland and Victoria. Nonetheless, clear association between separate or combined large-scale climate predictors and South Australian spring rainfall is yet to be established. Past studies showed that maximum rainfall predictability was only 20% considering isolated/individual effects of ENSO and SAM predictors in this region. The present study further explored these hypotheses by investigating two additional important aspects: investigating the relationship between lagged individual climate predictors with spring rainfall and linked (multiple combinations of ENSO and SAM) influences of significant lagged-climate indicators on spring rainfall forecasting using multiple regression (MR) modeling. Three stations were chosen as case studies for this region. MR models with combined-lagged climate predictors (SOI-SAM based models) showed better forecast ability in both model calibration and validation periods for all the stations. Results demonstrated that rainfall predictability significantly increased using combined climate predictor’s influence compared to their individual effect. It was discovered that rainfall predictability increased up-to 63% using combined climate predictors compared to their single influences. The maximum attained rainfall predictability for the SOI-SAM based models was 47% for calibration period that significantly enhanced with combined predictors influence to 97% during validation period. Therefore, MR analyses delineated the capabilities and influences of remote climate drivers in forecasting South Australian spring rainfall.