We examine the characteristics and causes of southeast Australia’s Tinderbox Drought (2017 to 2019) that preceded the Black Summer fire disaster. The Tinderbox Drought was characterized by cool season rainfall deficits of around −50% in three consecutive years, which was exceptionally unlikely in the context of natural variability alone. The precipitation deficits were initiated and sustained by an anomalous atmospheric circulation that diverted oceanic moisture away from the region, despite traditional indicators of drought risk in southeast Australia generally being in neutral states. Moisture deficits were intensified by unusually high temperatures, high vapor pressure deficits, and sustained reductions in terrestrial water availability. Anthropogenic forcing intensified the rainfall deficits of the Tinderbox Drought by around 18% with an interquartile range of 34.9 to −13.3% highlighting the considerable uncertainty in attributing droughts of this kind to human activity. Skillful predictability of this drought was possible by incorporating multiple remote and local predictors through machine learning, providing prospects for improving forecasting of droughts.

Droughts are associated with large‐scale modes of variability, synoptic‐scale systems, and terrestrial processes. Quantifying their relative roles in influencing drought guides process understanding, helps identify weaknesses in climate models, and focuses model improvements. Using a Lagrangian back‐trajectory approach we provide the first quantification of the change in moisture supply during major droughts in southeast Australia, including the causes of the changes. Drought onset and intensification were driven by reduced moisture supply from the ocean, as moisture was circulated away from the region, combined with an absence of precipitation‐generating mechanisms over land. During termination, strengthened moist easterly flows from the Tasman and Coral Seas promoted anomalously high rainfall. Our approach reveals terrestrial moisture sources played a secondary role, amplifying rainfall anomalies by less than 6%. Simulating droughts therefore requires deeper understanding of the relationship between moisture advection and synoptic‐scale circulation and how large‐scale climate variability and terrestrial processes modify these relationships.

Modes of climate variability can drive significant changes to regional climate affecting extremes such as droughts, floods, and bushfires. The need to forecast these extremes and expected future increases in their intensity and frequency motivates a need to better understand the physical processes that connect climate modes to regional precipitation. Focusing on east Australia, where precipitation is driven by multiple interacting climate modes, this study provides a new perspective into the links between large-scale modes of climate variability and precipitation. Using a Lagrangian back-trajectory approach, we examine how El Niño–Southern Oscillation (ENSO) modifies the supply of evaporative moisture for precipitation, and how this is modulated by the Indian Ocean dipole (IOD) and southern annular mode (SAM). We demonstrate that La Niña modifies large-scale moisture transport together with local thermodynamic changes to facilitate local precipitation generation, whereas below-average precipitation during El Niño stems predominantly from increased regional subsidence. These dynamic–thermodynamic processes were often more pronounced during co-occurring La Niña/negative IOD and El Niño/positive IOD periods. As the SAM is less strongly correlated with ENSO, the impact of co-occurring ENSO and SAM largely depended on the state of ENSO. La Niña–related processes were exacerbated when combined with +SAM and dampened when combined with −SAM, and vice versa during El Niño. This new perspective on how interacting climate modes physically influence regional precipitation can help elucidate how model biases affect the simulation of Australian climate, facilitating model improvement and understanding of regional impacts from long-term changes in these modes. Significance Statement: How climate modes modulate the oceanic and terrestrial sources of moisture for rainfall in east Australia is investigated. East Australia is wetter during La Niña because more moisture is transported into the region and is more easily turned into rainfall when it arrives, whereas drier conditions during El Niño are because local conditions inhibit the conversion of moisture into rainfall. Distant atmospheric changes over the Indian and Southern Oceans can intensify these changes. Our results can be used to better understand and predict the regional impact of long-term changes in these modes of climate variability, which are potentially altered under climate change.

El Niño‐Southern Oscillation (ENSO) is the main driver of interannual east Australian rainfall variability, but its link with rain‐producing synoptic weather systems is unclear. By tracking low pressure systems in ERA5 over 1979 to 2021, we find that springtime cyclones are linked to variations in the large‐scale atmospheric circulation during ENSO events. On spring days with a cyclone during La Niña, a pressure dipole occurs with a strong anticyclonic anomaly southeast of Australia and a cyclonic anomaly over eastern Australia. The northeasterly circulation directs tropical moisture toward eastern Australia, and coupled with induced ascent, promotes rainfall in this region. Both dynamical and thermodynamical changes are important for the rainfall response. An almost opposite circulation response occurs on cyclone days during El Niño events: high‐pressure over the Australian continent reduces rainfall in eastern Australia. These synoptic setups resemble the seasonal‐mean Rossby wave teleconnections, indicating a link between weather systems and ENSO.

McArthur’s Forest Fire Danger Index (FFDI) incorporates the Keetch–Byram Drought Index (KBDI) estimate of soil dryness. Improved approaches for estimating soil moisture now exist, with potential for informing the calculation of FFDI. We evaluated the effect, compared with KBDI, of two alternative methods of estimating soil moisture: the rainfall-based Antecedent Precipitation Index and soil moisture from the Soil Moisture Ocean Salinity satellite mission. These methods were used to calculate FFDI over a sample period of 5 years (2010–14) at seven locations around Australia. The effect of substituting the alternatives for KBDI, and of entirely replacing the Drought Factor (DF) (a measure of fuel availability in FFDI) with the alternatives was explored by studying the effect on magnitude, distribution and timing of FFDI and associated Fire Danger Rating (FDR). Both approaches predicted drier soil conditions than KBDI, resulting in fewer Low–Moderate FDR days and more days of High FDR and above. The alternative methods replacing KBDI had little effect on seasonal patterns of FDR. Of all approaches, replacing DF entirely with the soil moisture alternatives most closely mimicked McArthur’s FFDI. Overall, if alternative measures of soil moisture are adopted for FFDI, the entire replacement of the DF term should be considered.