Southeast Australia is frequently impacted by drought, requiring monitoring of how the various factors influencing drought change over time. Precipitation and temperature trends were analysed for Canberra, Australia, revealing decreasing autumn precipitation. However, annual precipitation remains stable as summer precipitation increased and the other seasons show no trend. Further, mean temperature increases in all seasons. These results suggest that Canberra is increasingly vulnerable to drought. Wavelet analysis suggests that the El-Niño Southern Oscillation (ENSO) influences precipitation and temperature in Canberra, although its impact on precipitation has decreased since the 2000s. Linear regression (LR) and support vector regression (SVR) were applied to attribute climate drivers of annual precipitation and mean maximum temperature (TMax). Important attributes of precipitation include ENSO, the southern annular mode (SAM), Indian Ocean Dipole (DMI) and Tasman Sea SST anomalies. Drivers of TMax included DMI and global warming attributes. The SVR models achieved high correlations of 0.737 and 0.531 on prediction of precipitation and TMax, respectively, outperforming the LR models which obtained correlations of 0.516 and 0.415 for prediction of precipitation and TMax on the testing data. This highlights the importance of continued research utilising machine learning methods for prediction of atmospheric variables and weather pattens on multiple time scales.

The aims of this study are to assess the impacts of accelerated climate change on summer maximum temperatures since the early 1990s in the Australian city of Sydney’s eastern coastal and western inland suburbs. Western Sydney currently experiences far more intense summer (December–March) heat waves than coastal Sydney, with maximum temperatures exceeding those of coastal Sydney by up to 10 °C. Aside from increased bushfire danger, extreme temperature days pose health and socio-economic threats to western Sydney. Permutation tests of consecutive summer periods, 1962–1991 and 1992–2021, are employed to determine the differential climate change impacts on maximum summer temperatures at two locations: Sydney and Richmond, representative of eastern and western Sydney, respectively. Attribution of observed maximum summer temperature trends in Sydney and Richmond was performed using machine learning techniques applied to known Australian region oceanic and atmospheric climate drivers. It was found that there is a marked disparity in the percentage of summer days above the 95th percentile during the accelerated climate change period (1992–2021) between Richmond (+35%) and Sydney (−24%), relative to 1962–1991. The climate drivers detected as attributes were similar in both Sydney and Richmond, but, unsurprisingly, Sydney was more affected than Richmond by the oceanic climate drivers.

Flash droughts (FDs) are natural disasters that strike suddenly and intensify quickly. They occur almost anywhere, anytime of the year, and can have severe socio-economic, health and environmental impacts. This study focuses on a recent FD that began in the cool season of the Upper Hunter region of Eastern Australia, an important energy and agricultural local and global exporter that is both flood- and drought-prone. Here, the authors investigate the FD that started abruptly in May 2023 and extended to October 2023. The FD followed floods in November 2021 and much above-average May–October 2022 rainfall. Eight machine learning (ML) regression techniques were applied to the 60 May–October periods from 1963–2022, using a rolling windows attribution search from 45 possible climate drivers, both individually and in combination. The six most prominent climate drivers, and likely predictors, provide an understanding of the major contributors to the FD. Next, the 1963–2022 data were divided into two shorter timespans, 1963–1992 and 1993–2022, generally accepted as representing the early and accelerated global warming periods, respectively. The key attributes were markedly different for the two timespans. These differences are readily explained by the impacts of global warming on hemispheric and synoptic-scale atmospheric circulations.

Low pressure systems off the southeast coast of Australia can generate intense rainfall and associated flooding, destructive winds, and coastal erosion, particularly during the cool season (April–September). Impacts depend on coastal proximity, strength and latitude. Therefore, it is important to investigate changes in frequency, duration, location, and intensity of these systems. First, an existing observation-based database of these low pressure systems, for 1970–2006, is extended to 2019, focusing on April–September and using archived Australian Bureau of Meteorology MSLP charts. Second, data consistency between 1970 and 2006 and 2007 and 2019 is confirmed. Third, permutation testing is performed on differences in means and variances between the two 25-year intervals 1970–1994 and 1995–2019. Additionally, trends in positions, durations and central pressures of the systems are investigated. p-values from permutation tests reveal statistically significant increases in mean low pressure system frequencies. Specifically, a greater frequency of both total days and initial development days only, occurred in the latter period. Statistically significant lower variance for both latitude and longitude in systems that developed in both subtropical easterly and mid-latitude westerly wind regimes indicate a shift south and east in the latter period. Furthermore, statistically significant differences in variance of development location of explosive low pressure systems that develop in a low level easterly wind regime indicate a shift further south and east. These changes are consistent with fewer systems projected to impact the east coast. Finally, important changes are suggested in the large scale atmospheric dynamics of the eastern Australian/Tasman Sea region.

Droughts and long dry spells, interspersed with intense rainfall events, have been characteristic of the northern Murray-Darling Basin (NMDB), a major Australian agricultural region. The NMDB precipitation results from weather systems ranging from thunderstorms to larger scale events. The larger scale events exhibit high seasonal and annual rainfall variability. To detect attributes shaping the NMDB precipitation patterns, and hence net water inflows to the vast Darling River catchment area, numerous (45) possible attributes were assessed for their influence on rainfall trends. Four periods were assessed: annual, April–May (early cool-season), June–September (remaining cool-season), and October–March (warm-season). Linear and non-linear regression machine learning (ML) methods were used to identify the dominant attributes. We show the impact of climate drivers on the increasingly dry April–May months on annual precipitation and warmer temperatures since the early 1990s. The NMDB water supply was further reduced during 1992–2018 by the lack of compensating rainfall trends for the April–May decline. The identified attributes include ENSO, the Southern Annular Mode, the Indian Ocean Dipole, and both local and global sea surface temperatures. A key finding is the prominence of global warming as an attribute, both individually and in combination with other climate drivers.

Droughts in southeastern Australia can profoundly affect the water supply to Sydney, Australia’s largest city. Increasing population, a warming climate, land surface changes and expanded agricultural use increase water demand and reduce catchment runoff. Studying Sydney’s water supply is necessary to manage water resources and lower the risk of severe water shortages. This study aims at understanding Sydney’s water supply by analysing precipitation and temperature trends across the catchment. A decreasing trend in annual precipitation was found across the Sydney catchment area. Annual precipitation also is significantly less variable, due to fewer years above the 80th percentile. These trends result from significant reductions in precipitation during spring and autumn, especially over the last 20 years. Wavelet analysis was applied to assess how the influence of climate drivers has changed over time. Attribute selection was carried out using linear regression and machine learning techniques, including random forests and support vector regression. Drivers of annual precipitation included Niño3.4, Southern Annular Mode (SAM) and DMI, and measures of global warming such as the Tasman Sea sea surface temperature anomalies. The support vector regression model with a polynomial kernel achieved correlations of 0.921 and a skill score compared to climatology of 0.721. The linear regression model also performed well with a correlation of 0.815 and skill score of 0.567, highlighting the importance of considering both linear and non-linear methods when developing statistical models. Models were also developed on autumn and winter precipitation but performed worse than annual precipitation on prediction. For example, the best performing model on autumn precipitation, which accounts for approximately one quarter of annual precipitation, achieved an RMSE of 418.036 mm${}^2$ on the testing data, while annual precipitation achieved an RMSE of 613.704 mm${}^2$. However, the seasonal models provided valuable insights into whether the season would be wet or dry compared to the climatology.