Australia is a signatory to the Paris Agreement, a legally binding international treaty on climate change adopted in December 2015. The accord culminated from two weeks of intensive multilateral negotiations at the United Nations Climate Change Conference (COP21). While endorsed by 194 UN member states alongside the European Union, only Iran, Libya, and Yemen refrained from signing. Although the United States was an initial signatory, it remains the sole nation to have formally exited the pact via an executive order issued by President Trump in January 2025. The primary objective of the Paris Agreement is to mitigate greenhouse gas (GHG) emission to stabilize global temperature increases well below 2 °C relative to pre-industrial baselines. Ultimately, the treaty aims to achieve global net-zero emissions by mid-century to restrict the temperature rise to 1.5 °C. To ensure compliance, the Agreement establishes a robust Transparency Framework for tracking, reviewing, and reporting the climate actions undertaken by signatory nations. Furthermore, developed economies committed to mobilizing a dedicated climate finance fund to assist developing nations in executing their respective mitigation and adaptation strategies (Framework Convention on Climate Change, 2015). To curb GHG emissions, most states in Australia have heavily prioritized the large-scale deployment of residential rooftop photovoltaic (PV) systems. Approximately 31% to 32% of homes in the State of Victoria have implemented rooftop solar panel generation. Official data from Solar Victoria and the Australian Photovoltaic Institute indicates that PV penetration in Victoria has reached this mark, reflecting more than 850,000 rooftop solar systems installed across the state (Solar Victoria, 2026a). This paper examines the performance characteristics of these installations and develops a predictive model to assess their future generation capacity.

Most climatic models show that climate change affects natural perils' frequency and severity. Quantifying the impact of future climate conditions on natural hazard is essential for mitigation and adaptation planning. One crucial factor to consider when using climate simulations projections is the inherent systematic differences (bias) of the modelled data compared with observations. This bias can originate from the modelling process, the techniques used for downscaling of results, and the ensembles' intrinsic variability. Analysis of climate simulations has shown that the biases associated with these data types can be significant. Hence, it is often necessary to correct the bias before the data can be reliably used for further analysis. Natural perils are often associated with extreme climatic conditions. Analysing trends in the tail end of distributions are already complicated because noise is much more prominent than that in the mean climate. The bias of the simulations can introduce significant errors in practical applications. In this paper, we present a methodology for bias correction of climate simulated data. The technique corrects the bias in both the body and the tail of the distribution (extreme values). As an illustration, maps of the 50 and 100-year Return Period of climate simulated Forest Fire Danger Index (FFDI) in Australia are presented and compared against the corresponding observation-based maps. The results show that the algorithm can substantially improve the calculation of simulation-based Return Periods. Forthcoming work will focus on the impact of climate change on these Return Periods considering future climate conditions.

The hazard produced by natural phenomena on infrastructure and urban populations has been widely studied. Researchers recognise that the danger posed by these phenomena depends on their extreme values. Most researchers focus on the extremes of natural phenomena considered in isolation, one variable at a time. What is relevant in hazard studies is, however, coincident extremes of several climatic variables, i.e. the presence of compound extremes. The peak value of these extremes seldom coincide, but off-peak values located in the tail of the distributions are often concurrent and can lead to catastrophic events. The problem is complicated by the presence of correlations between these variables. This paper presents a robust methodology to solve the problem based on the convolution of distributions. To illustrate the methodology, the case of a cyclone landing close to a coastal city is discussed, and its probability of flooding and recurrence period is calculated.