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A multi-scenario, multi-model ensemble of simulations from regional climate models is outlined to provide the core data source for a set of climate projections and a climate change service. A subset of realisations from CMIP6 Global Climate Models (GCMs) are selected for downscaling by Regional Climate Models (RCMs) under a ‘sparse matrix’ framework using the CORDEX guidelines for Shared Socio-economic Pathways that feature low emissions (SSP1-2.6) and high emissions (SSP3-7.0). The subset excludes poor performing models, with performance assessed by the climatology over a large Indo-Pacific domain and an Australian-specific domain, the simulation of atmospheric circulation and teleconnections to major drivers, then incorporating other evaluation from the literature. The models are selected to be relatively independent by simply choosing one model from each ‘family’ where possible. The projected change in temperature and rainfall in climatic regions of Australia in the selected models are broadly representative of that from the whole CMIP6 ensemble, after deliberately treating models with very high climate sensitivity separately. A limited but carefully constructed ensemble will not represent statistically balanced estimates but can be used effectively under a ‘storylines’ style approach and can maximise representativeness within limits. The resulting ensemble can be used as a key data source for the future climate component of climate services in Australia. The ensemble will be used in conjunction with CMIP6 and large ensembles of GCM simulations as important context, and targeted ‘convective permitting resolution’ modelling, deep learning models and emulators for added insights to inform climate change planning in Australia.

Abstract High‐resolution regional reanalysis datasets have the potential to provide valuable guidance to emergency management agencies, highlighting areas at risk of severe weather, including estimates of return periods of various hazardous weather phenomena. The BARRA regional reanalysis for Australia comprises a reanalysis for a broad region around Australia at moderately high spatial and temporal resolution (12 km/hourly), together with four subdomains at high resolution (1.5 km/1 h). Here, we document four applications of BARRA developed for emergency management: optimal placement of portable automatic weather stations for fire weather monitoring; climatology of low‐level wind shear conducive to cool‐season tornadogenesis; development of rainfall intensity–frequency–duration curves based on the gridded reanalysis data; and development of a climatology across Australia of parameters associated with severe thunderstorm occurrence.

Due to the limited availability of discharge data in many catchments globally, it is important to develop a calibration method that does not rely solely on discharge data. Motivated by this limitation, two calibration approaches using water level data directly in hydrological calibrations are proposed in the study. The first is a Spearman Rank correlation (SRC) based scheme, which calibrates modelled streamflow against observed water level using Spearman Rank correlation. The second is an Inverse Rating Curve (IRC) function based scheme, which introduces three more parameters to simulate water level from an inverse rating curve. The new approaches are tested in 11 catchments in Australia and the resulting discharge predictions show good correlation with observations. However, the results present large biases between observations and estimated discharge data due to the inherent limitation of the approach: absence of information on the true discharge range in the calibration process. To mitigate the biases, the magnitude-sensitive SRC/IRC-based schemes that incorporate a small number of observations are developed in this study. The bias issue is then mitigated significantly, but the improvement is not consistent throughout the examined catchments. One of the most critical challenges of the bias correction is that the whole dynamic range of discharge is constrained by a few observed discharge data, but overall, the new calibration approaches using only water level data prove to be promising.