Decision-makers urgently need fit-for-purpose, actionable climate information to address worsening risks to water security due to climate change. To help address this need, we pioneer new work that builds on a recently established standardized benchmarking framework to assess downscaled precipitation simulations and explore how benchmarking historical, fundamental model performance impacts confidence in regional precipitation projections. Inspired by frameworks used by the Coupled Model Intercomparison Project (CMIP) community to navigate the “ensemble of opportunity” that explore the spread, independence, and performance of CMIP models, we assess future confidence across three related categories: model agreement, dependence, and performance. Using data from the Coordinated Regional Downscaling Climate Experiments (CORDEX)-Australasia domain and Southern Australia as a case study, we highlight how benchmarking only “fundamental,” historical model performance improves confidence in regional precipitation projections across these categories of confidence. We find that including fundamentally flawed models in an ensemble can indicate a false level of confidence and model agreement in regional climate projections in annual and wet-season total rainfall and rainfall extremes. We find that our benchmarked subset reduces the spread in projections across season-rainfall index combinations without underestimating observed natural variability or end-of-century uncertainties due to the climate change response. By taking a novel approach to address the coupled interdependencies between global climate models and regional climate models in our dynamically downscaled ensemble, we find that we can increase confidence though historical benchmarking spatially and temporally without over-constraining plausible future projections.

In this study, we employ the Conformal Cubic Atmospheric Model (CCAM), a variable-resolution global atmospheric model, driven by two distinct sea surface temperature (SST) datasets: the 0.25° Optimum Interpolation Sea Surface Temperature (CCAM_OISST) version 2.1 and the 2° Extended Reconstruction SSTs Version 5 (CCAM_ERSST5). Model performance is assessed using a benchmarking framework, revealing good agreement between both simulations and the climatological rainfall spatial pattern, seasonality, and annual trends obtained from the Australian Gridded Climate Data (AGCD). Notably, wet biases are identified in both simulations, with CCAM_OISST displaying a more pronounced bias. Furthermore, we have examined CCAM’s ability to capture El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) correlations with rainfall during Austral spring (SON) utilizing a novel hit rate metric. Results indicate that only CCAM_OISST successfully replicates observed SON ENSO- and IOD-rainfall correlations, achieving hit rates of 86.6% and 87.5%, respectively, compared to 52.7% and 41.8% for CCAM_ERSST5. Large SST differences are found surrounding the Australian coastline between OISST and ERSST5 (termed the “Coastal Effect”). Differences can be induced by the spatial interpolation error due to the discrepancy between model and driving SST. An additional CCAM experiment, employing OISST with SST masked by ERSST5 in 5° proximity to the Australian continent, underscores the “Coastal Effect” has a significant impact on IOD-Australian rainfall simulations. In contrast, its influence on ENSO-Australian rainfall is limited. Therefore, simulations of IOD-Australian rainfall teleconnection are sensitive to local SST representation along coastlines, probably dependent on the spatial resolution of driving SST.

Marine heatwave (MHW) characteristics - such as frequency, intensity and duration - are increasing, largely due to global warming. However, while corresponding marine cold spell (MCS) characteristics have decreased over most regions, importantly, concomitant changes in MHW and MCS characteristics remain unclear. Here, we provide a comparative global assessment of these changes based on satellite sea surface temperature (SST) observations over 1982-2020. Across the planet, we find distinct differences in mean MHW and MCS metrics. Furthermore, decreasing trends in MCS characteristics are not necessarily aligned with increasing trends in MHW characteristics. While differences in intensity trends are mainly explained by SST variance trends, differences in exposure trends are less clear. Overall, decreasing MCS exposure and intensities are found to be largely driven by warming SST, rather than changes to SST variance, so it is expected that MCS will continue to diminish in their frequency, intensity and duration under global warming.

Outputs from new state-of-the-art climate models under the Coupled Model Inter-comparison Project phase 6 (CMIP6) promise improvement and enhancement of climate change projections information for Australia. Here we focus on three key aspects of CMIP6: what is new in these models, how the available CMIP6 models evaluate compared to CMIP5, and their projections of the future Australian climate compared to CMIP5 focussing on the highest emissions scenario. The CMIP6 ensemble has several new features of relevance to policy-makers and others, for example the integrated matrix of socio-economic and concentration pathways. The CMIP6 models show incremental improvements in the simulation of the climate in the Australian region, including a reduced equatorial Pacific cold-tongue bias, slightly improved rainfall teleconnections with regional climate drivers, improved representation of atmosphere and ocean extreme heat events, as well as dynamic sea level. However, important regional biases remain, evident in the excessive precipitation over the Maritime Continent and precipitation pattern biases in the nearby tropical convergence zones. Projections of temperature and rainfall from the available CMIP6 ensemble broadly agree with those from CMIP5, except for a group of CMIP6 models with higher climate sensitivity and greater warming and increase in some extremes after 2050. CMIP6 rainfall projections are similar to CMIP5, but the ensemble examined has a narrower range of rainfall change in summer in the north and winter in the south. Overall, future national projections are likely to be similar to previous versions but perhaps with some areas of improved confidence and clarity.