Three protracted droughts have occurred during the instrumental history of Southeast Australia (SEA) – the “Federation” (∼1895–1902), “World War II” (∼1937–1945) and the “Big Dry” (∼1997–present). This paper compares the nature and causes of these droughts in order to better inform drought management strategies in SEA. It is shown that the three droughts differ in terms of severity, spatial footprint, seasonality and seasonal rainfall make‐up. This diversity arises due to the fact that the droughts are driven by different climatic teleconnections with the Pacific, Indian and Southern Oceans. Importantly, this study highlights potential flaws with drought forecasting and management in SEA and emphasises the need for further research into understanding and representing hydroclimatic drivers of drought.

Abstract. Isolating the causes of extreme variations or changes in the hydroclimate is difficult due to the complexities of the driving mechanisms, but it is crucial for effective natural resource management. In Australia's Murray-Darling Basin (MDB), ocean-atmosphere processes causing hydroclimatic variations occur on time scales from days to centuries, all are important, and none are likely to act in isolation. Instead, interactions between all hydroclimatic drivers, on multiple time scales, are likely to have caused the variations observed in MDB instrumental records. A simplified framework is presented to assist natural resource managers in identifying the potential causes of hydroclimatic anomalies. The framework condenses an event into its fundamental elements, including its spatial and temporal signal and smallscale evolution. The climatic processes that are potentially responsible are then examined to determine possible causes. The framework was applied to a period of prolonged and severe dry conditions occurring in the southern MDB from 1997-2010, providing insights into possible causal mechanisms that are consistent with recent studies. The framework also assists in identifying uncertainties and gaps in our understanding that need to be addressed.

Abstract. Since the mid-1990s the majority of Victoria, Australia, has experienced severe drought conditions (i.e. the "Big Dry") characterized by streamflow that is the lowest in approximately 80 years of record. While decreases in annual and seasonal rainfall totals have also been observed, this alone does not seem to explain the observed reduction in flow. In this study, we investigate the large-scale climate drivers for Victoria and demonstrate how these modulate the regional scale synoptic patterns, which in turn alter the way seasonal rainfall totals are compiled and the amount of runoff per unit rainfall that is produced. The hydrological implications are significant and illustrate the need for robust hydrological modelling, that takes into account insights into physical mechanisms that drive regional hydroclimatology, in order to properly understand and quantify the impacts of climate change (natural and/or anthropogenic) on water resources.

Abstract. Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods). This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets -the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. The results of the monthly, seasonal and annual comparisons show that not only are the three gridded datasets different relative to each other, there are also marked differences between the gridded rainfall data and the rainfall observed at gauges within the corresponding grids -particularly for extremely wet or extremely dry conditions. Also important is that the differences observed appear to be non-systematic. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia initially using gauged data as the source of rainfall input and then gridded rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged data. Rather, the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.

Abstract. The Southern Annular Mode (SAM) has been identified as a climate mechanism with potentially significant impacts on the Australian hydroclimate. However, despite the identification of relationships between SAM and Australia's hydroclimate using certain data sets, and focussed on certain time periods, the association has not been extensively explored and significant uncertainties remain. One reason for this is the existence of numerous indices, methods and data sets by which SAM has been approximated. In this paper, the various SAM definitions and indices are reviewed and the similarities and discrepancies are discussed, along with the strengths and weaknesses of each index development approach. Further, the sensitivity of the relationship between SAM and Australian rainfall to choice of SAM index is quantified and recommendations are given as to the most appropriate index to use when assessing the impacts of the SAM on Australia's hydroclimate. Importantly this study highlights the need to consider the impact that the choice of SAM index, and data set used to calculate the index, has on the outcomes of any SAM attribution study.

Abstract. In this paper regional (synoptic) and large-scale climate drivers of rainfall are investigated for Victoria, Australia. A non-linear classification methodology known as self-organizing maps (SOM) is used to identify 20 key regional synoptic patterns, which are shown to capture a range of significant synoptic features known to influence the climate of the region. Rainfall distributions are assigned to each of the 20 patterns for nine rainfall stations located across Victoria, resulting in a clear distinction between wet and dry synoptic types at each station. The influence of large-scale climate modes on the frequency and timing of the regional synoptic patterns is also investigated. This analysis revealed that phase changes in the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and/or the Southern Annular Mode (SAM) are associated with a shift in the relative frequency of wet and dry synoptic types on an annual to interannual timescale. In addition, the relative frequency of synoptic types is shown to vary on a multi-decadal timescale, associated with changes in the Inter-decadal Pacific Oscillation (IPO). Importantly, these results highlight the potential to utilise the link between the regional synoptic patterns derived in this study and large-scale climate modes to improve rainfall forecasting for Victoria, both in the short-(i.e. seasonal) and long-term (i.e. decadal/multi-decadal scale). In addition, the regional and large-scale climate drivers identified in this study provide a benchmark by which the performance of Global Climate Models (GCMs) may be assessed.

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Tropical cyclones (TCs) pose a significant risk to the nations and territories of the southwest Pacific (SWP). The spatio‐temporal variability of TCs makes it challenging to forecast where and when a TC is likely to develop. Therefore, the aim of this study is to better understand the link between large‐scale climatic variability, the environmental conditions required for tropical cyclogenesis (TC genesis), and the spatial variability of TC activity. Three modes of climate variability are investigated: (1) El Niño/Southern Oscillation (ENSO), (2) ENSO Modoki, and (3) the Interdecadal Pacific Oscillation (IPO); along with TC genesis parameters: sea surface temperature; 700 hPa relative humidity; 700 hPa vorticity; and vertical wind shear (difference between 200 hPa and 850 hPa winds). Our findings reaffirm the well‐established northeast/southwest modulation of TC activity according to El Niño (EN)/La Niña (LN) using an extended TC dataset (1945–2011). In addition, new insights into how ENSO Modoki and the IPO modulate TC activity according to phase and season are identified. Importantly, we show that depending on phase, the IPO can enhance or alter the spatial modulation of TC genesis during ENSO/ENSO Modoki events, in favour of the northeast/southwest modulations typical of IPO positive/negative events. This is particularly the case during the latter half of the SWP TC season. For example, EN events that occur within IPO positive (negative) epochs result in a shift of TC activity up to 1087 km (1288 km) further east (west) during February to April, compared to the typical location of TC activity during EN events. Importantly, these statistical relationships are also associated with anomalously favourable genesis parameters, providing some insights into the physical mechanisms behind the modulations. The findings of this study provide baseline metrics with which to compare climate model simulations and may also facilitate improved seasonal outlooks and better quantification of TC‐related risks for the vulnerable island nations and territories of the SWP.