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High‐resolution climate change projections are increasingly necessary to inform climate policy and adaptation planning. Downscaling of global climate models (GCMs) is required to simulate the climate at the spatial scale relevant for local impacts. Here, we dynamically downscaled 15 CMIP6 GCMs to a 10 km resolution over Australia using the Conformal Cubic Atmospheric model (CCAM), creating the largest ensemble of high‐resolution downscaled CMIP6 projections for Australia. We compared the host CMIP6 models and downscaled simulations to the Australian Gridded Climate Data (AGCD) observational data and evaluated performance using the Kling‐Gupta efficiency and Perkins skill score. Downscaling improved performance over host GCMs for seasonal temperature and precipitation (10% and 43% respectively), and for annual cycles of temperature and precipitation (6% and 13% respectively). Downscaling also improved the fraction of dry days, reducing the bias for too many low‐rain days. The largest improvements were found in climate extremes, with enhancements to extreme minimum temperatures in all seasons varying from 142% to 201%, and to extreme precipitation of 52% in Austral winter and 47% in summer. The ensemble average integrated skill score improved by 16%. Temperature and precipitation biases were reduced in mountainous and coastal areas. CCAM downscaling outperformed host CMIP6 GCMs at multiple spatial scales and regions—continental Australia, Australian IPCC regions and Queensland's regions—with integrated added value ranging from 9% to 150% and higher over densely populated regions more exposed to climate impacts. This data set will be a valuable resource for understanding future climate changes in Australia. Plain Language Summary: High‐resolution climate models are used for producing climate change projections for assessing regional and local climate change impacts and for adaptation and policy formulation. We completed 15 high‐resolution climate simulations using the Conformal Cubic Atmospheric Model at a 10 km spatial resolution over the Australian continent and surrounds based on global climate models (GCMs) used in the Intergovernmental Panel on Climate Change Assessment Report 6. We evaluated the new high‐resolution data set by comparing it to observations and to their host global model across several regions within Australia. The high‐resolution simulations improved the representation of topography, seasonal temperature and annual cycles of temperature, precipitation, as well as the number of dry days, extreme precipitation and extreme minimum temperatures. The high‐resolution projections show improvement compared to GCMs consistently across spatial scales and regions, being particularly high (150%) over urban areas with high exposure to impacts. This new 10 km data set is the highest resolution, and largest ensemble of the latest climate projections for the Australian continent and surrounds and will be a valuable tool for evaluating future climate change impacts. Key Points: We dynamically downscaled 15 CMIP6 global climate model simulations over Australia to a 10 km spatial resolution using the Conformal Cubic Atmospheric modelTwo new assessment metrics are proposed to assess model performance and added value of downscalingThe integrated added value of downscaling can be as high as 150% over highly populated areas [ABSTRACT FROM AUTHOR]

There is low confidence in projections of Antarctic sea ice area (SIA), due to deficiencies in climate model sea ice processes. Ensemble regression techniques can help to reduce this uncertainty. We investigate relationships between SIA climatology and 21st century change in the Coupled Model Intercomparison Project, phase 6 (CMIP6) multi‐model ensemble. In summer, under a strong forcing scenario, each model loses the majority of its sea ice. Therefore, models with greater historical SIA exhibit greater reductions, so the observed climatology of SIA strongly constrains projections. Ensemble spread in historical summer SIA is smaller than in CMIP phase 5 (CMIP5), and CMIP6 gives a more robust constraint on future SIA. In winter, by 2100 under a strong forcing scenario, 40% of SIA disappears on average, and ensemble spread in historical mean SIA explains approximately half the spread in projected change. A greater winter ice loss in CMIP6 than CMIP5 is explained by the higher climate sensitivities of some CMIP6 models. Plain Language Summary: Antarctic sea ice area is important for ecosystems, human activity, and for the dynamics of the atmosphere and ocean. However, major climate change assessments such as the Intergovernmental Panel on Climate Change Sixth Assessment Report have placed little confidence in projections of Antarctic sea ice area. This is because climate models struggle to simulate many aspects of sea ice area as observed by satellites. However, not all models should be treated equally. Models with relatively more sea ice area in the recent past project greater losses in the future, especially in summer. This suggests that models with historical sea ice area closest to what we have observed may be more reliable in their future simulations. We use this information in a simple statistical model to show that, firstly, the newest climate models largely show near‐total sea ice loss in summer by the end of the 21st century, which was not universally true for older climate models. Secondly, newer climate models lose more winter sea ice than their predecessors in the same time period, which is related to the known greater global warming response to given greenhouse gas concentrations in the newer models. Key Points: We compare Coupled Model Intercomparison Project (CMIP), phase 5 and the CMIP, phase 6 (CMIP6) projections of Antarctic sea ice loss, assessing drivers of uncertainty and the robustness of projectionsUnder strong forcing, CMIP6 models lose most Antarctic summer sea ice by 2100, so projections are constrained by historical climatologyIncreased winter sea ice loss in CMIP6 is related to increased equilibrium climate sensitivity [ABSTRACT FROM AUTHOR]

Tropical coral reefs are among the most sensitive ecosystems to climate change and will benefit from the more ambitious aims of the United Nations Framework Convention on Climate Change's Paris Agreement, which proposed to limit global warming to 1.5° rather than 2°C above pre‐industrial levels. Only in the latest Intergovernmental Panel on Climate Change focussed assessment, the Coupled Model Intercomparison Project phase 6 (CMIP6), have climate models been used to investigate the 1.5° warming scenario directly. Here, we combine the most recent model updates from CMIP6 with a semi‐dynamic downscaling to evaluate the difference between the 1.5 and 2°C global warming targets on coral thermal stress metrics for the Great Barrier Reef (GBR). By ~2080, severe bleaching events are expected to occur annually under intensifying emissions (shared socioeconomic pathway SSP5‐8.5). Adherence to 2° warming (SSP1‐2.6) halves this frequency but the main benefit of confining warming to 1.5° (SSP1‐1.9) is that bleaching events are reduced further to 3 events per decade. Attaining low emissions of 1.5° is also paramount to prevent the mean magnitude of thermal stress from stabilizing close to a critical thermal threshold (8 Degree Heating Weeks). Thermal stress under the more pessimistic pathways SSP3‐7.0 and SSP5‐8.5 is three to fourfold higher than the present day, with grave implications for future reef ecosystem health. As global warming continues, our projections also indicate more regional warming in the central and southern GBR than the far north and northern GBR. [ABSTRACT FROM AUTHOR]

We analyze projected changes in climate extremes (extreme temperatures and heavy precipitation) in the multimodel ensembles of the fifth and sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6). The results reveal close similarity between both ensembles in the regional climate sensitivity of the projected multimodel mean changes in climate extremes, that is, their projected changes as a function of global warming. This stands in contrast to widely reported divergences in global (transient and equilibrium) climate sensitivity in the two multimodel ensembles. Some exceptions include higher warming in the South America monsoon region, lower warming in Southern Asia and Central Africa, and higher increases in heavy precipitation in Western Africa and the Sahel region in the CMIP6 ensemble. The multimodel spread in regional climate sensitivity is found to be large in both ensembles. In particular, it contributes more to intermodel spread in projected regional climate extremes compared with the intermodel spread in global climate sensitivity in CMIP6. Our results highlight the need to consider regional climate sensitivity as a distinct feature of Earth system models and a key determinant of projected regional impacts, which is largely independent of the models' response in global climate sensitivity. Plain Language Summary: Many articles analyze and compare global climate sensitivity in climate models, that is, how their global warming differs at a given level of CO2 concentrations. However, global warming is only one quantity affecting impacts. To assess human‐ and ecosystem‐relevant impacts, it is essential to evaluate the regional climate sensitivity of climate models, that is, how their regional climate features differ at a given level of global warming. We analyze here regional climate sensitivity in the new multimodel ensemble that will underlie the conclusions of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This ensemble of model projections is called the "Sixth Coupled Model Intercomparison Project" or CMIP6. We find that differences in regional climate sensitivity between models in CMIP6 often contribute more to the uncertainty of regional extremes projections than the uncertainty in global mean warming between models. Overall, the regional climate sensitivity features in the CMIP6 models' projections ensemble are very similar to those of the prior ensemble (CMIP5), although the model ensembles have been highlighted to differ in their global climate sensitivity over the 21st century. Key Points: Changes in climate extremes as a function of global warming are quasilinear and determine a "regional climate sensitivity" in CMIP5 and CMIP6The regional climate sensitivity of climate extremes is found to be very similar in CMIP5 and CMIP6, unlike global climate sensitivityModel spread in regional climate sensitivity in CMIP6 contributes more to uncertainty of projected extremes than global climate sensitivity [ABSTRACT FROM AUTHOR]