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.

The diurnal cycle is often poorly reproduced in global climate model (GCM) simulations, particularly in terms of rainfall frequency and amplitude. While improvements in the regional climate model (RCM) with bias-corrected boundaries have been reported in previous studies, they assumed that diurnal patterns are simulated correctly by the GCM, potentially leading to inaccuracies in the maximum rainfall timing and magnitude within the RCM domain. Here we provide the first examination of improvements to the diurnal cycle, within a RCM domain, achieved through the use of sophisticated bias-corrected lateral and lower boundary conditions. Results show that the RCMs with bias-corrected boundaries generally present improvement in capturing both rainfall timing and magnitude, particularly in northern Australia, where a strong diurnal pattern in rainfall is prevalent. We show that correcting systematic sub-daily multivariate bias in RCM boundaries improves the diurnal rainfall cycle, which is particularly important in regions where short-term intense precipitation occurs.

Preparing for environmental risks requires estimating the frequencies of extreme events, often from data records that are too short to confirm them directly. This requires fitting a statistical distribution to the data. To improve precision, investigators often pool data from neighboring sites into single samples, referred to as “superstations,” before fitting. We demonstrate that this technique can introduce unexpected biases in typical situations, using wind and rainfall extremes as case studies. When the combined locations have even small differences in the underlying statistics, the regionalization approach gives a fit that may tend toward the highest levels suggested by any of the individual sites. This bias may be large or small compared to the sampling error, for realistic record lengths, depending on the distribution of the quantity analysed. The results of this analysis indicate that previous analyses could potentially have overestimated the likelihood of extreme events arising from natural weather variability.

Solar photovoltaic (PV) energy is one of the most preferred and fastest growing emission-free energy sources in Australia. However, the dependency of PV generation on climatological factors can impact future power generation. Considering the future large-scale deployment of PV systems, accurate climate information is essential for PV site selection, stable grid regulation, planning and energy output projections. In this study, the effect of projected changes in shortwave downwelling radiation, temperature and wind speed on the performance of PV systems over Australia is examined using regional climate projections. Results indicate a small, but significant decline in future PV potential over Australia due to reduced insolation and increased temperature. Under a high emission scenario, the cell temperature is predicted to increase 2.5°C by 2070 leading to increased periods of significantly reduced cell efficiency (6-13 days/year) mostly in Western and central Australia.

Although observations and modelling studies show that heavy rainfall is increasing in many regions, how changes will manifest themselves on sub-daily timescales remains highly uncertain. Here, for the first time, we combine observational analysis and high-resolution modelling results to examine changes to extreme rainfall intensities in urbanized Kuala Lumpur, Malaysia. We find that hourly intensities of extreme rainfall have increased by ~35% over the last three decades, nearly three times more than in surrounding rural areas, with daily intensities showing much weaker increases. Our modelling results confirm that the urban heat island effect creates a more unstable atmosphere, increased vertical uplift and moisture convergence. This, combined with weak surface winds in the Tropics, causes intensification of rainfall extremes over the city, with reduced rainfall in the surrounding region.