What is already known:
What this study adds:
In the past few decades, the climate in Australian has been warming at an alarming rate when compared to historical variations. Associated with that warming, extended heat events, lasting for weeks to months have plagued the country. Climate model projections suggest that such events will occur more frequently and intensify in the future. The extreme temperatures have damages ecosystems through droughts and fire and resulted in the loss of human life.
This study examines how the combination of sea surface temperatures (SSTs) and climate drivers predict summer mean maximum temperature at selected locations in SE Australia. Ninety-one ocean grid boxes of SST surrounding Australia were used for simultaneous and lag1 relations as well as 42 climate drivers, creating a suite of 224 potential predictors. Variable reduction using 5-fold cross validated linear regression and bagging, resulted in ~ 90% reduction in the number of variables passed to the final prediction equations. Linear multiple and nonlinear kernel regression methods were applied to predict the January anomalies of maximum temperature using this reduced set of predictors. For the nonlinear regressions, two kernels were evaluated: polynomial and radial basis function. The polynomial degree and radial basis function kernel width were optimized for sea surface temperatures and climate drivers by maximizing their 10-fold cross validated correlations with the air temperatures at the various locations in SE Australia. The key findings were (1) climate drivers had as much significant influence on the prediction accuracy as SSTs and (2) the combination of the reduced sets of SSTs and climate drivers often accounted for 40-60% of the January mean maximum temperature variance. Such a large percentage of predictable variance is expected to lead to more effective monthly temperature predictions.