Depending on the phase of various climate modes, South Australia is susceptible to extreme climatic conditions including heat waves, bushfires and drought. Recent studies have focused on climate variability and change in the southeast and southwest of Australia. The individual and combined effects of specific climate drivers on South Australian climate are not well understood. This project aims to identify and evaluate the long-term trends of climate drivers significantly affecting South Australian climate variation and change as well as the major oscillation patterns of these drivers. Remote drivers include the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO) the Southern Annular Mode (SAM), and global warming (GW). Local drivers include large scale atmospheric blocking and the subtropical ridge (STR).

Multiple regression stepwise models are employed to investigate the individual and combined effect of climate drivers on rainfall variation throughout annual, seasonal and monthly periods in South Australia generally as well as the Adelaide region and the northern part of the State. Both the SAM and GW display clear increasing trends over the last 60 years, likely to be linked to increased greenhouse gas concentrations. A positive trend is observed in South Australian rainfall during the spring and summer months, while a reduction in rainfall is seen during the winter and autumn months. Overall, atmospheric blocking shows the highest correlations to South Australian rainfall variation, particularly in the spring and in the Adelaide region. The effects of the SAM are also felt throughout the year. ENSO plays a small role during the summer and spring months, mostly in the northern part of the State, but is largely inactive during the winter or autumn period. Rather, the IOD and STR intensity, as well as atmospheric blocking and SAM, are the main drivers of winter rainfall. The importance of the relative drivers to rainfall variation changes with time. Outcomes indicate that GW, the SAM and in particular the atmospheric blocking are becoming stronger drivers of South Australian rainfall.

While regression model outputs provide a good indication of estimated rainfall anomalies once significant drivers have been identified for a particular region, the complexities of meteorologic phenomena are unlikely to be fully represented in this simple statistical method. Meaningful predictions relying on this technique are also limited to models where significant drivers are found to be relatively consistent in their trend and influence on rainfall variation over time.