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Cities can have a significant impact on local microclimate. Higher temperatures that often characterise urban fabric can influence other meteorological parameters, such as precipitation. In this study, we investigated how the urban heat island (UHI) of Kuala Lumpur impacts rainfall through a set of sensitivity studies performed with the Weather Research and Forecasting (WRF) model. Many studies have already pointed out that the UHI can increase local rainfall, but they disregarded the city heterogeneity to large extent. Here, we investigated the effect of the city on precipitation incorporating different representations of the urban landscape. We performed three simulations with different urban land cover: 1) without city (control experiment) 2) with the urban terrain represented homogeneously and 3) with the urban land represented heterogeneously with the surface classification in the 11 categories of the Local Climate Zone (LCZ) system. We observed that the consideration of the city of Kuala Lumpur in the simulations results in a localised increase in mean annual precipitation and mean intense precipitation within the boundaries of the urban area. However, in the case of the homogeneous representation of the city, the increase is more pronounced than in the case of the heterogeneously represented city. In the former case, the increases also occur over a larger area and the impacts propagate more strongly into the upper layers of the atmosphere. Thus, a more realistic representation of the city and its heterogeneities limits the urban-induced effects on precipitation.

On 15 January 2022, around 4:30 UTC the eruption of the Hunga-Tonga volcano, in the South Pacific Ocean, generated a violent underwater explosion. In addition to tsunami waves that affected the Pacific coasts, the eruption created atmospheric pressure disturbances that spread out in the form of Lamb waves. The associated atmospheric pressure oscillations were detected in high-frequency in-situ observations all over the globe. Here we take advantage of the similarities in the propagation and characteristics between atmospheric Lamb waves and long ocean waves and we use a 2DH ocean numerical model to simulate the phenomenon. We compare the outputs of the numerical simulation with in-situ atmospheric pressure records and with remote satellite observations. The signal in the model matches the observed atmospheric pressure perturbations and reveals an excellent agreement in the wave arrival time between model and observations at hundreds of locations at different distances from the origin., This study was supported by the MOCCA project Grant RTI2018-093941-B-C31 funded by MCIN/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe". It was also supported by grants PGC2018-099285-B-C21 and PGC2018-099285-B-C22 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” NextGenerationEU/PRTR. Angel Amores was funded by the Conselleria d’Educació, Universitat i Recerca del Govern Balear through the Direcció General de Política Universitària i Recerca and by the Fondo Social Europeo for the period 2014–2020 (Grant No. PD/011/2019). Daniel Argüeso was funded by Spanish Ministry of Science and Innovation through the EPICC Project (PID2019-105253RJ-I00) and the Beatriz Galindo Programme (BG20/00078).

Heavy rainfall is among the most impactful natural events. Our understanding of such events has improved significantly in the last decades, but large uncertainties remain around their recent and future response to a changing climate. At global scales, the frequency and intensity of daily extreme precipitation has increased, the hydrological cycle is becoming faster. However, the response at regional scales and shorter timescales is much more complex. The study of sub-daily or even sub-hourly data has been explored to some extent only, mostly due to the limited availability of data. When using high-resolution models to explore rainfall changes, it is possible to examine much higher frequencies, yet most studies focus on daily rainfall changes.Here, we demonstrate inherent limitations of daily data to study present and future precipitation extremes. Limitations that are not purely a matter of refining our sampling, but do have a physical background because outstanding rainfall rates rarely occur over the course of a day. Our results show that fundamental aspects of rainfall changes are not described with daily data, and the assessment of future changes in daily precipitation likely leads to misrepresentation of causes and impacts. We show that the short-lived and intermittent nature of most rainfall extremes need at least hourly data to be properly characterized, otherwise heavy rainfall is poorly detected. Analyzing higher frequencies also reveals aspects of extremes that cannot be addressed with daily data, such as changes in their intensity and duration. This is particularly relevant for risk and impact assessment studies because a significant part of changes in extremes occur at sub-daily scales. Such changes go unnoticed or, even worse, are misrepresented by daily rainfall amounts.

Approximately 10 years ago, convection-permitting regional climate models (CPRCMs) emerged as a promising computationally affordable tool to produce fine resolution (1-4 km) decadal-long climate simulations with explicitly resolved deep convection. This explicit representation is expected to reduce climate projection uncertainty related to deep convection parameterizations found in most climate models. A recent surge in CPRCM decadal simulations over larger domains, sometimes covering continents, has led to important insights into CPRCM advantages and limitations. Furthermore, new observational gridded datasets with fine spatial and temporal (similar to 1 km; similar to 1 h) resolutions have leveraged additional knowledge through evaluations of the added value of CPRCMs. With an improved coordination in the frame of ongoing international initiatives, the production of ensembles of CPRCM simulations is expected to provide more robust climate projections and a better identification of their associated uncertainties. This review paper presents an overview of the methodology to produce CPRCM simulations and the latest research on the related added value in current and future climates. Impact studies that are already taking advantage of these new CPRCM simulations are highlighted. This review paper ends by proposing next steps that could be accomplished to continue exploiting the full potential of CPRCMs. This article is categorized under: Climate Models and Modeling > Earth System Models

The importance of resolving mesoscale air-sea interactions to represent cyclones impacting the East Coast of Australia, the so-called East Coast Lows (ECLs), is investigated using the Australian Regional Coupled Model based on NEMO-OASIS-WRF (NOW) at urn:x-wiley:2169897X:media:jgrd57355:jgrd57355-math-0001 resolution. The fully coupled model is shown to be capable of reproducing correctly relevant features such as the seasonality, spatial distribution and intensity of ECLs while it partially resolves mesoscale processes, such as air-sea feedbacks over ocean eddies and fronts. The mesoscale thermal feedback (TFB) and the current feedback (CFB) are shown to influence the intensity of northern ECLs (north of urn:x-wiley:2169897X:media:jgrd57355:jgrd57355-math-0002), with the TFB modulating the pre-storm sea surface temperature by shifting ECL locations eastwards and the CFB modulating the wind stress. By fully uncoupling the atmospheric model of NOW, the intensity of northern ECLs is increased due to the absence of the cold wake that provides a negative feedback to the cyclone. The number of ECLs might also be affected by the air-sea feedbacks but large interannual variability hampers significant results with short term simulations. The TFB and CFB modify the climatology of sea surface temperature (mean and variability) but no direct link is found between these changes and those noticed in ECL properties. These results show that the representation of ECLs, mainly north of urn:x-wiley:2169897X:media:jgrd57355:jgrd57355-math-0003, depend on how air-sea feedbacks are simulated. This is particularly important for atmospheric downscaling of climate projections as small-scale sea surface temperature interactions and the effects of ocean currents are not accounted for.

A fundamental issue when evaluating the simulation of precipitation is the difficulty of quantifying specific sources of errors and recognizing compensation of errors. We assess how well a large ensemble of high‐resolution simulations represents the precipitation associated with strong cyclones. We propose a framework to breakdown precipitation errors according to different dynamical (vertical velocity) and thermodynamical (vertically integrated water vapor) regimes and the frequency and intensity of precipitation. This approach approximates the error in the total precipitation of each regime as the sum of three terms describing errors in the large‐scale environmental conditions, the frequency of precipitation and its intensity. We show that simulations produce precipitation too often, that its intensity is too weak, that errors are larger for weak than for strong dynamical forcing and that biases in the vertically integrated water vapor can be large. Using the error breakdown presented above, we define four new error metrics differing on the degree to which they include the compensation of errors. We show that convection‐permitting simulations consistently improve the simulation of precipitation compared to coarser‐resolution simulations using parameterized convection, and that these improvements are revealed by our new approach but not by traditional metrics which can be affected by compensating errors. These results suggest that convection‐permitting models are more likely to produce better results for the right reasons. We conclude that the novel decomposition and error metrics presented in this study give a useful framework that provides physical insights about the sources of errors and a reliable quantification of errors.

Renewable energy is a main avenue to reduce greenhouse gas emissions and mitigate climate change, as well as health impacts, associated with mining, refining and burning fossil fuels. Isolated locations with consistent natural energy resources patterns, such as Hawaii, have great potential to reduce their dependence on fossil fuels and generate energy locally. Using a regional atmospheric model, we explored the wind-power potential of Oahu at high resolution (1 km) and over a period (2005–2014) that allowed the assessment of variability from hourly to interannual. A validation of the model using both weather stations and wind farms showed the need for observational data at the turbine hub height to correctly estimate model errors for wind power applications because the model response can be quite different at standard near-surface wind measurement heights. The model performance at larger timescales evidences the potential for long-term assessment of wind characteristics. On the other hand, the model errors at sub-daily timescales indicated limitations of short-term planning, except for sudden changes in wind speed, which were accurately simulated. Our results identify optimal locations for wind power plants from capacity factor estimates, which include analysis of mean, variability at different timescales, ramps, and sustained periods of low generation.

International audience; Like many western boundary currents, the East Australian Current (EAC) extension is projected to get stronger and warmer in the future. The CMIP5 multimodel mean (MMM) projection suggests up to 5°C of warming under an RCP85 scenario by 2100. Previous studies employed Sverdrup balance to associate a trend in basin wide zonally integrated wind stress curl (resulting from the multidecadal poleward intensification in the westerly winds over the Southern Ocean) with enhanced transport in the EAC extension. Possible regional drivers are yet to be considered. Here we introduce the NEMO-OASIS-WRF coupled regional climate model as a framework to improve our understanding of CMIP5 projections. We analyze a hierarchy of simulations in which the regional atmosphere and ocean circulations are allowed to freely evolve subject to boundary conditions that represent present-day and CMIP5 RCP8.5 climate change anomalies. Evaluation of the historical simulation shows an EAC extension that is stronger than similar ocean-only models and observations. This bias is not explained by a linear response to differences in wind stress. The climate change simulations show that regional atmospheric CMIP5 MMM anomalies drive 73% of the projected 12 Sv increase in EAC extension transport whereas the remote ocean boundary conditions and regional radiative forcing (greenhouse gases within the domain) play a smaller role. The importance of regional changes in wind stress curl in driving the enhanced EAC extension is consistent with linear theory where the NEMO-OASIS-WRF response is closer to linear transport estimates compared to the CMIP5 MMM. Plain Language Summary In recent decades, enhanced warming, severe marine heatwaves, and increased transport by the East Australian Current have led to the invasion of nonnative species and the destruction of kelp forests east of Tasmania. The East Australian Current extension is projected to get stronger and warmer in the future. We seek to better understand coupled climate model projections for the Tasman Sea. This is difficult because there is large model diversity and considerable uncertainty as to how and where future changes will occur. In addition, little is known about the possible importance of regional versus large-scale changes in surface time-mean winds in driving future circulation changes. Here we use a single limited-domain ocean-atmosphere coupled model that takes the average model projections as its inputs and finds that changes in the regional wind stress are most important for the enhanced projected East Australian Current extension. We also find that these projected changes are consistent with simple linear theory and the simulated regional changes in wind stress.

The Maritime Continent is a major convective area and precipitation processes in the region pose great challenges to atmospheric models. A combination of large-scale drivers, such as the Madden-Julian Oscillation and ENSO, and fine-scale processes, such as orographically-forced precipitation, land-sea circulations and tropical convection, governs rainfall in the Maritime Continent. The use of convection-permitting models in the region has shown improved performance in the simulation of precipitation characteristics that are key for the region (i.e. diurnal cycle).Most of the rainfall occurring over land is concentrated in the late afternoon and precipitation extremes often occur over short periods of time. The availability of water vapor in the lower troposphere and the high water-holding capacity of a warm atmosphere favors very intense precipitation events, according to the Clausius-Clapeyron relationship. In a warming climate, a full understanding of the so-called precipitation scaling with temperature is thus crucial. However, this potential generally requires the atmosphere be saturated and convection be initiated to become effective. Using a regional climate model operating at convection-permitting scales over 3 consecutive wet seasons, we investigate the response of intense precipitation to temperature.In this presentation, we examine different approaches to relate precipitation extremes to near-surface temperature and dew-point temperature. We show that the relationship breaks at certain thresholds that are relatively uniform across islands. The region is well supplied with water vapor and the break is not explained by a deficit in water vapor, unlike previously proposed for other water-limited regions. We identify possible reasons for this behavior, such as the lack of environmental conditions that trigger convection. In this context, we explore the sensitivity of the modelling system to the convection representation (explicit vs. parameterized) and discuss the implications for future changes in intense precipitation events. Finally, we put forward the use of specific variables, such as temperature and equivalent potential temperature integrated in the vertical. These variables not only are coherent with the CC equation but also acknowledge the different warming rates near the surface and at higher tropospheric levels, where precipitating processes actually occur.

Air pollution has significant impacts on human health. Temperature inversions, especially near surface temperature inversions, can amplify air pollution by preventing convective movements and trapping pollutants close to the ground, thus decreasing air quality and increasing health issues. This effect of temperature inversions implies that trends in their frequency, strength and duration can have important implications for air quality. In this study, we evaluate the ability of three reanalysis-driven high-resolution regional climate model (RCM) simulations to represent near surface inversions at 9 sounding sites in southeast Australia. Then we use outputs of 12 historical and future RCM simulations (each with three time periods: 1990?2009, 2020?2039, and 2060?2079) from the NSW/ACT (New South Wales/Australian Capital Territory) Regional Climate Modelling (NARCliM) project to investigate changes in near surface temperature inversions. The results show that there is a substantial increase in the strength of near surface temperature inversions over southeast Australia which suggests that future inversions may intensify poor air quality events. Near surface inversions and their future changes have clear seasonal and diurnal variations. The largest differences between simulations are associated with the driving GCMs, suggesting that the large-scale circulation plays a dominant role in near surface inversion strengths. Fil: Ji, Fei. New South Wales. Office of Environment and Heritage; Australia Fil: Evans, Jason Peter. University of New South Wales; Australia Fil: Di Luca, Alejandro. University of New South Wales; Australia Fil: Jiang, Ningbo. New South Wales. Office of Environment and Heritage; Australia Fil: Olson, Roman. Yonsei University; Corea del Sur Fil: Fita Borrell, Lluís. Centre National de la Recherche Scientifique; Francia. Universite Pierre et Marie Curie; Francia. Laboratoire de Meteorologie Dynamique; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Argüeso, Daniel. Universidad de Las Islas Baleares. Departamento de Fisica; España Fil: Chang, Lisa T. C.. New South Wales. Office of Environment and Heritage; Australia Fil: Scorgie, Yvonne. New South Wales. Office of Environment and Heritage; Australia Fil: Riley, Matt. New South Wales. Office of Environment and Heritage; Australia

The ability of regional climate models (RCMs) to accurately simulate current and future climate is increasingly important for impact assessment. This is the first evaluation of all reanalysis-driven RCMs within the CORDEX Australasia framework [four configurations of the Weather Forecasting and Research (WRF) model, and single configurations of COSMO-CLM (CCLM) and the Conformal-Cubic Atmospheric Model (CCAM)] to simulate the historical climate of Australia (1981–2010) at 50 km resolution. Simulations of near-surface maximum and minimum temperature and precipitation were compared with gridded observations at annual, seasonal, and daily time scales. The spatial extent, sign, and statistical significance of biases varied markedly between the RCMs. However, all RCMs showed widespread, statistically significant cold biases in maximum temperature which were the largest during winter. This bias exceeded − 5 K for some WRF configurations, and was the lowest for CCLM at ± 2 K. Most WRF configurations and CCAM simulated minimum temperatures more accurately than maximum temperatures, with biases in the range of ± 1.5 K. RCMs overestimated precipitation, especially over Australia’s populous eastern seaboard. Strong negative correlations between mean monthly biases in precipitation and maximum temperature suggest that the maximum temperature cold bias is linked to precipitation overestimation. This analysis shows that the CORDEX Australasia ensemble is a valuable dataset for future impact studies, but improving the representation of land surface processes, and subsequently of surface temperatures, will improve RCM performance. The varying RCM capabilities identified here serve as a foundation for the development of future regional climate projections and impact assessments for Australia.

This study presents future changes in extreme precipitation as projected within the New South Wales and Australian Capital Territory Regional Climate Modelling (NARCliM) project’s regional climate ensemble for south-east Australia. Model performance, independence and projected future changes were considered when designing the ensemble. We applied a quantile mapping bias correction to the climate model outputs based on theoretical distribution functions, and the implications of this for the projected precipitation extremes is investigated. Precipitation extremes are quantified using several indices from the Expert Team on Climate Change Detection and Indices set of indices. The bias correction was successful in removing most of the magnitude bias in extreme precipitation but does not correct biases in the length of maximum wet and dry spells. The bias correction also had a relatively small effect on the projected future changes. Across a range of metrics, robust increases in the magnitude of precipitation extreme indices are found. While these increases are often in-line with a continuation of the trends present over the last century, they are not found to be statistically significant within the ensemble as a whole. The length of the maximum consecutive wet spell is projected to remain at present-day levels, while the length of the maximum dry spell is projected to increase into the future. The combination of longer dry spells and increases in extreme precipitation magnitude indicate an important change in the character of the precipitation time series. This could have considerable hydrological implications since changes in the sequencing of events can be just as important as changes in event magnitude for hydrological impacts.

Increased temperature will result in longer, more frequent, and more intense heat waves. Changes in temperature variability have been deemed necessary to account for future heat wave characteristics. However, this has been quantified only in Europe and North America, while the rest of the globe remains unexplored. Using late century global climate projections, we show that annual mean temperature increases is the key factor defining heat wave changes in most regions. We find that commonly studied areas are an exception rather than the standard and the mean climate change signal generally outweighs any influence from variability changes. More importantly, differences in warming across seasons are responsible for most of the heat wave changes and their consideration relegates the contribution of variability to a marginal role. This reveals that accurately capturing mean seasonal changes is crucial to estimate future heat waves and reframes our interpretation of future temperature extremes.

This study evaluates the added value in the representation of surface climate variables from an ensemble of regional climate model (RCM) simulations by comparing the relative skill of the RCM simulations and their driving data over a wide range of RCM experimental setups and climate statistics. The methodology is specifically designed to compare results across different variables and metrics, and it incorporates a rigorous approach to separate the added value occurring at different spatial scales. Results show that the RCMs’ added value strongly depends on the type of driving data, the climate variable, and the region of interest but depends rather weakly on the choice of the statistical measure, the season, and the RCM physical configuration. Decomposing climate statistics according to different spatial scales shows that improvements are coming from the small scales when considering the representation of spatial patterns, but from the large-scale contribution in the case of absolute values. Our results also show that a large part of the added value can be attained using some simple postprocessing methods.

Regional climate model (RCM) simulations are often used with agricultural models to assess the impact of climate change on agriculture. This study assesses the suitability of climate data sets from an RCM ensemble for forcing simulations of wheat yields for New South Wales, Australia, performed using the Agricultural Production Systems sIMulator (APSIM). Differences in yields between APSIM simulations forced with RCM output for the 1990–2009 period and APSIM simulations forced with observations are examined, as are simulated changes in yields between 1990–2009 and 2060–2079. The RCM ensemble downscales four global climate models (GCMs) using three different RCMs, firstly to a horizontal resolution of approximately 50 km, and then to approximately 10 km. All of the RCM simulations, 50 and 10 km, are able to simulate the spatial pattern of yields across the study region. However, some simulations have biases in yields. The largest of these are due to biases in rainfall during the growing season inherited from the GCMs. If the RCM output is bias-corrected, the largest positive yield biases are reduced because the underlying biases in growing season rainfall are reduced. Simulated future changes in yields are affected because there is a non-linear relationship between simulated yields and growing season rainfall. Downscaling to 10 km, rather than 50 km, is only beneficial when bias correction is used. The bias-correction technique used does not eliminate all biases in growing season rainfall and increases some of the smaller biases. Nonetheless, because of the potentially large effect of the rainfall biases on simulated future yield changes, this study supports the use of bias correction in assessments of the impacts of climate change that use RCM output to force APSIM. Indeed, our findings suggest that bias correction may be necessary to obtain reliable future changes in other outputs of other biophysical models that respond non-linearly to climate inputs.

The occurrence of very high temperatures (hot extremes) is often linked with negative impacts in human health, natural ecosystems and the economy (e.g., energy, water supply and agriculture). Studies have invariably shown that the intensity and frequency of hot extremes will increase in the future thus increasing their associated risks. While much progress has been made in quantifying and understanding hot temperature extremes and their future changes, there are still open questions. This paper focusses on the sources of hot extremes and their changes by applying a simple and unambiguous methodology that describes daily hot extremes as the superposition of four well known physical terms that include information on the annual mean temperature, the amplitude of the annual cycle, the diurnal temperature range and the local temperature anomaly on the day of the extreme. The methodology was applied to 30-year daily temperature records from 6 observation-based datasets and 31 atmosphere-ocean global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The comparison between observed and simulated hot extremes shows a remarkably consistent picture where most CMIP5 models overestimate the term describing the local temperature extreme anomaly over most regions of the globe regardless of the observed dataset considered. Simultaneously, CMIP5 models show a systematic cold bias in the annual mean temperature and in the diurnal temperature range terms leading to substantial error compensation over some regions. This prompted us to define a new error estimator as the sum of errors in individual terms that appears to be much more effective at characterising model's performance compared to the traditional bias estimator. The assessment of future changes in hot extremes shows that changes are dominated by changes in annual mean temperatures with varying contributions from the other terms that strongly depend on the specific region being considered. Western Europe appears as a hot spot for extreme temperature changes (increases of ~ 8 ∘C by the end of the century) due to significant contributions from all decomposition terms including the summer mean anomaly, the diurnal temperature range and the daily extreme anomaly. Tropical South America also appears as a hot spot for extreme temperature changes (increases of ~ 7 ∘C) largely due to an increase in the daily extreme anomaly term (explaining about 30% of the full change) making this region one of the most sensitive regions in the world in terms of hot extremes. The analysis reveals that the separation of future changes according to terms describing mean, variability and tails is very sensitive to the specific way the mean component is defined including assumptions about stationarity.

Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensemble members that can be simulated such that choices must be made concerning which global climate models (GCMs) to downscale from, and which regional climate models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCM and RCM ensembles, as well as spanning the range of future climate projections present in the GCM ensemble. The RCM selection process uses performance evaluation metrics to eliminate poor performing models from consideration, followed by explicit consideration of model independence in order to retain as much information as possible in a small model subset. In addition to these two steps the GCM selection process also considers the future change in temperature and precipitation projected by each GCM. The final GCM selection is based on a subjective consideration of the GCM independence and future change. The created ensemble provides a more robust view of future regional climate changes. Future research is required to determine objective criteria that could replace the subjective aspects of the selection process.

This study investigates the effects of urban areas on precipitation in the western Maritime Continent using a convection-permitting regional atmospheric model. The Weather Research and Forecasting model was used to simulate the atmosphere at a range of spatial resolutions using a multiple nesting approach. Two experiments (with and without urban areas) were completed over a 5-year period (2008–2012) each to estimate the contribution of cities to changes in local circulation. At first, the model is evaluated against two satellite-derived precipitation products and the benefit of using a very high-resolution model (2-km grid spacing) over a region where rainfall is dominated by convective processes is demonstrated, particularly in terms of its diurnal cycle phase and amplitude. The influence of cities on precipitation characteristics is quantified for two major urban nuclei in the region (Jakarta and Kuala Lumpur) and results indicate that their presence locally enhances precipitation by over 30 %. This increase is mainly due to an intensification of the diurnal cycle. We analyse the impact on temperature, humidity and wind to put forward physical mechanisms that explain such changes. Cities increase near surface temperature, generating instability. They also make land-sea temperature contrasts stronger, which enhances sea breeze circulations. Together, they increase near-surface moisture flux convergence and favour convective processes leading to an overall increase of precipitation over urban areas. The diurnal cycle of these effects is reflected in the atmospheric footprint of cities on variables such as humidity and cloud mixing ratio and accompanies changes in precipitation.

While it is established that low-frequency climate variability modes have a dominant role on Australia's climate, limited work to date has focused on relationships between climate variability and Australian heatwaves. Moreover, heatwaves are a distinctive type of extreme weather that can be classified by multiple characteristics, such as intensity, frequency, duration, and timing. This study identifies the relationships between known modes of climate variability that influence Australian climate, and discrete seasonal characteristics of the intensity, frequency, duration, and timing of heatwaves. The large-scale seasonal modes of the El Nino/Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Southern Annular Mode (SAM) are investigated for extended Austral summers commencing between the years 1911 and 2012. While ENSO is found to have the strongest relationship with Australian heatwave characteristics, this study finds that ENSO's influence differs between heatwave frequency, duration, intensity, and timing. Regions dominated by ENSO experience more, longer lasting and hotter heatwaves combined with an earlier commencement of the heatwave season during El Nino phases. The exception to this is southeast Australia, where SAM is generally more dominant. In contrast, the IOD provides little indication of seasonal heatwave characteristics due to its relative inactivity during the Austral summer months. Lastly, we show that antecedent soil moisture has varying strengths of relationships with Australian heatwave characteristics, exhibiting relationships with heatwave intensity and timing over some regions where none are detected between large-scale modes. However, while significant relationships between dry antecedent soil moisture and extreme heatwaves do exist over Australia, these appear to be slightly weaker than similar relationships over Europe reported in other studies.

Identifying the relationship between large-scale climate signals and seasonal streamflow may provide a valuable tool for long-range seasonal forecasting in regions under water stress, such as the Iberian Peninsula (IP). The skill of the main teleconnection indices as predictors of seasonal streamflow in the IP was evaluated. The streamflow database used was composed of 382 stations, covering the period 1975–2008. Predictions were made using a leave-one-out cross-validation approach based on multiple linear regression, combining Variance Inflation Factor and Stepwise Backward selection to avoid multicollinearity and select the best subset of predictors. Predictions were made for four forecasting scenarios, from one to four seasons in advance. The correlation coefficient (RHO), Root Mean Square Error Skill Score (RMSESS), and the Gerrity Skill Score (GSS) were used to evaluate the forecasting skill. For autumn streamflow, good forecasting skill (RHO>0.5, RMSESS>20%, GSS>0.4) was found for a third of the stations located in the Mediterranean Andalusian Basin, the North Atlantic Oscillation of the previous winter being the main predictor. Also, fair forecasting skill (RHO>0.44, RMSESS>10%, GSS>0.2) was found in stations in the northwestern IP (16 of these located in the Douro and Tagus Basins) with two seasons in advance. For winter streamflow, fair forecasting skill was found for one season in advance in 168 stations, with the Snow Advance Index as the main predictor. Finally, forecasting was poorer for spring streamflow than for autumn and winter, since only 16 stations showed fair forecasting skill in with one season in advance, particularly in north-western of IP.

East Coast Lows (ECLs) are intense low- pressure systems that affect the eastern seaboard of Australia. They have attracted research interest for both their destructive nature and water supplying capabil- ity. Estimating the changes in ECLs in the future has a major impact on emergency response as well as water management strategies for the coastal communities on the east coast of Australia. In this study, ECLs were identi- fied using two large-scale diagnostic quantities: isen- tropic potential vorticity (IPV) and geostrophic vorticity (GV), which were calculated from outputs of historical and future regional climate simulations from the NSW/ ACT regional climate modelling (NARCliM) project. The diagnostic results for the historical period were evaluated against a subjective ECL event database. Future simula- tions using a high emission scenario were examined to estimate changes in frequency, duration, and intensity of ECLs. The use of a relatively high resolution regional cli- mate model makes this the first study to examine future changes in ECLs while resolving the full range of ECL sizes which can be as small as 100-200 km in diameter. The results indicate that it is likely that there will be

The climate of the eastern seaboard of Australia is strongly influenced by the passage of low pressure systems over the adjacent Tasman Sea due to their associated precipitation and their potential to develop into extreme weather events. The aim of this study is to quantify differences in the climatology of east coast lows derived from the use of six global reanalyses. The methodology is explicitly designed to identify differences between reanalyses arising from differences in their horizontal resolution and their structure (type of forecast model, assimilation scheme, and the kind and number of observations assimilated). As a basis for comparison, reanalysis climatologies are compared with an observation-based climatology. Results show that reanalyses, specially high-resolution products, lead to very similar climatologies of the frequency, intensity, duration, and size of east coast lows when using spatially smoothed (about 300-km horizontal grid meshes) mean sea level pressure fields as input data. Moreover, at these coarse horizontal scales, monthly, interannual, and spatial variabilities appear to be very similar across the various reanalyses with a generally stronger agreement between winter events compared with summer ones. Results also show that, when looking at cyclones using reanalysis data at their native resolution (approaching 50-km grid spacing for the most recent products), uncertainties related to the frequency, intensity, and size of lows are very large and it is not clear which reanalysis, if any, gives a better description of cyclones. Further work is needed in order to evaluate the usefulness of the finescale information in modern reanalyses and to better understand the sources of their differences.

The Maritime Continent is a region of intense rainfall characterised by a strong diurnal cycle. This study investigates the sensitivity of rainfall characteristics to resolution in a coupled regional climate model configuration, in particular focusing on processes that modulate the diurnal cycle. Model biases are resolution dependent. Increasing resolution from 3/4° to 1/4° improves the mean state sea surface temperature and precipitation biases. However, at higher resolutions (1/12°) rainfall becomes too strong in most areas. Daily maximum rainfall is simulated about 2–4 h earlier than in observations over both the land and the ocean, with only small improvements over high topography at higher resolution. We develop a technique to examine cross-coastal processes associated with the rainfall diurnal cycle along all coastlines. This is used to investigate the sensitivity of the diurnal cycle to resolution and to the direction of the prevailing wind. During offshore prevailing winds, most land rainfall is confined near the coastline and associated with a shallow land-sea breeze circulation at all resolution (though rainfall partly develops directly inland at 1/12°). During onshore prevailing winds, rainfall propagates from the coastline to the island interior at 1/4° and 1/12°, whereas it appears directly over the island interior at 3/4°, and this is associated with a deep convective cell across the coastline for all resolutions. Oceanic rainfall propagates far offshore during offshore prevailing winds at all resolutions, whereas it tends to remain confined near the coastline under onshore prevailing winds condition, particularly at higher resolution.

International audience; NARCliM (New South Wales (NSW)/Australian Capital Territory (ACT) Regional Climate Modelling project) is a regional climate modeling project for the Australian area. It is providing a comprehensive dynamically downscaled climate dataset for the CORDEX-AustralAsia region at 50-km resolution, and south-East Australia at a resolution of 10 km. The first phase of NARCliM produced 60-year long reanalysis driven regional simulations to allow evaluation of the regional model performance. This long control period (1950-2009) was used so that the model ability to capture the impact of large scale climate modes on Australian climate could be examined. Simulations are evaluated using a gridded observational dataset. Results show that using model independence as a criteria for choosing atmospheric model configuration from different possible sets of parameterizations may contribute to the regional climate models having different overall biases. The regional models generally capture the regional climate response to large-scale modes better than the driving reanalysis, though no regional model improves on all aspects of the simulated climate.

Regional climate models are prone to biases in precipitation that are problematic for use in impact models such as hydrology models. A large number of methods have already been proposed aimed at correcting various moments of the rainfall distribution. They all require that the model produce the same or a higher number of rain days than the observational data sets, which are usually gridded data sets. Models have traditionally met this condition because their spatial resolution was coarser than the observational grids. But recent climate simulations use higher resolution and the models are likely to systematically produce fewer rain days than the gridded observations. In this study, model outputs from a simulation at 2 km resolution are compared with gridded and in situ observational data sets to determine whether the new scenario calls for revised methodologies. The gridded observations are found to be inadequate to correct the high-resolution model at daily timescales, because they are subjected to too frequent low intensity precipitation due to spatial averaging. A histogram equalisation bias correction method was adapted to the use of station, alleviating the problems associated with relative low-resolution observational grids. The wet-day frequency condition might not be satisfied for extremely dry biases, but the proposed approach substantially increases the applicability of bias correction to high-resolution models. The method is efficient at bias correcting both seasonal and daily characteristic of precipitation, providing more accurate information that is crucial for impact assessment studies.

This study examines the impact of future urban expansion on local near-surface temperature for Sydney (Australia) using a future climate scenario (A2). The Weather Research and Forecasting model was used to simulate the present (1990–2009) and future (2040–2059) climates of the region at 2-km spatial resolution. The standard land use of the model was replaced with a more accurate dataset that covers the Sydney area. The future simulation incorporates the projected changes in the urban area of Sydney to account for the expected urban expansion. A comparison between areas with projected land use changes and their surroundings was conducted to evaluate how urbanization and global warming will act together and to ascertain their combined effect on the local climate. The analysis of the temperature changes revealed that future urbanization will strongly affect minimum temperature, whereas little impact was detected for maximum temperature. The minimum temperature changes will be noticeable throughout the year. However, during winter and spring these differences will be particularly large and the increases could be double the increase due to global warming alone at 2050. Results indicated that the changes were mostly due to increased heat capacity of urban structures and reduced evaporation in the city environment.

As part of a special issue on natural hazards, this paper reviews the currentstate of scientific knowledge of Australian heatwaves. Over recent years, progress has been made in understanding both the causes of and changes to heatwaves. Relationships between atmospheric heatwaves and large-scale and synoptic variability have been identified, with increasing trends in heatwave intensity, frequency and duration projected to continue throughout the 21st century. However, more research is required to further our understanding of the dynamical interactions of atmospheric heatwaves, particularly with the land surface. Research into marine heatwaves is still in its infancy, with little known about driving mechanisms, and observed and future changes. In order to address these knowledge gaps, recommendations include: focusing on a comprehensive assessment of atmospheric heatwave dynamics; understandinglinks with droughts; working towards a unified measurement framework; and investigating observed and future trends in marine heatwaves. Such work requires comprehensive and long-term collaboration activities. However, benefits will extend to the international community, thus addressing global grand challenges surrounding these extreme events.

We examine how soil moisture variability and trends affect the simulation of temperature and precipitation extremes in six global climate models using the experimental protocol of the Global Land-Atmosphere Coupling Experiment of the Coupled Model Intercomparison Project, Phase 5 (GLACE-CMIP5). This protocol enables separate examinations of the influences of soil moisture variability and trends on the intensity, frequency, and duration of climate extremes by the end of the 21st century under a business-as-usual (Representative Concentration Pathway 8.5) emission scenario. Removing soil moisture variability significantly reduces temperature extremes over most continental surfaces, while wet precipitation extremes are enhanced in the tropics. Projected drying trends in soil moisture lead to increases in intensity, frequency, and duration of temperature extremes by the end of the 21st century. Wet precipitation extremes are decreased in the tropics with soil moisture trends in the simulations, while dry extremes are enhanced in some regions, in particular the Mediterranean and Australia. However, the ensemble results mask considerable differences in the soil moisture trends simulated by the six climate models. We find that the large differences between the models in soil moisture trends, which are related to an unknown combination of differences in atmospheric forcing (precipitation, net radiation), flux partitioning at the land surface, and how soil moisture is parameterized, imply considerable uncertainty in future changes in climate extremes. © 2015. American Geophysical Union. All Rights Reserved.

The ability of the Weather Research and Forecasting model (WRF) to simulate precipitation over Spain is evaluated from a climatological point of view. The complex topography and the large rainfall variability make the Iberian Peninsula a particularly interesting region and permit assessment of model performance under very demanding conditions. Three high-resolution (10 km) simulations over the Iberian Peninsula have been completed spanning a 30-yr period (1970–99) and driven by different datasets: the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) as “perfect boundary conditions” and two general circulation models (GCMs), the Max Planck Institute ECHAM5 model (ECHAM5/MPI) and the NCAR Community Climate System Model, version 3 (CCSM3). The daily precipitation observational grid Spain02 is employed to evaluate the model at varying time scales. Not only are the long-term means (annual, seasonal, and monthly) examined but also the high-order statistics (extreme events). The WRF provides valuable information on precipitation at high resolution and enhances local spatial distribution due to orographic features. Although substantial errors are still observed in terms of monthly precipitation, especially during the spring, the model is largely able to capture the various precipitation regimes. The major benefits of using WRF are related to the spatial distribution of rainfall and the simulation of extreme events, two facets of climate that can be barely explored with GCMs. This study shows that WRF can be a useful tool for generating high-resolution climate information for Spanish precipitation at spatial and temporal scales that are crucial for both the environment and human life.

Summary This paper investigates the spatial and temporal variability of streamflow in the Ebro river basin and its potential predictability. Principal Component Analysis applied to monthly streamflow series from 83 gauging stations distributed through the basin, reveals three homogeneous regions: Basque–Cantabrian, Pyrenees and Southern Mediterranean. Attending to this classification the main characteristic time scales of the maximum monthly streamflows are studied by Singular Spectral Analysis (SSA). Decadal variations in streamflow make particularly large contributions to year-to-year streamflow variance in stations placed in the Basque–Cantabrian and Southern Mediterranean regions, while for the Pyrenees flows the interannual contribution is more important. The predictability of the Ebro flow anomalies has been investigated using a combined methodology: at decadal time scales SST anomalies from several regions provide a significant source of predictability for the Ebro flow, while at interannual time scales autoregressive-moving-average modelling, applied to the time series previously filtered by SSA, is able to provide potential skill in forecasting. For gauging stations associated to the Basque–Cantabrian region significant correlations between the maximum monthly streamflow anomalies and a tripole-like pattern in the North Atlantic SSTs during the previous spring are found. This association is found maximum and stable for the tropical part of the pattern (approximately 0–20°N). For the gauging stations placed to the southeast of basin some influence from the Pacific Decadal Oscillation (PDO) is found. This method allows evaluating, independently, the decadal and interannual predictability of the streamflow series. In addition, the combination of both modelling techniques gives as result a methodology that has the capacity to provide basin-specific hydroclimatic predictions which vary (for the 1990–2003 validation period) between 62% for the Basque–Cantabrian region, 76% for the Southern Mediterranean and 81% for the Pyrenees. In summary, this work shows the existence of a valuable decadal and interannual predictability of the Ebro streamflow, a result which may be useful to water resources management.

This paper evaluates the Weather Research and Forecasting model (WRF) sensitivity to eight different combinations of cumulus, microphysics, and planetary boundary layer (PBL) parameterization schemes over a topographically complex region in southern Spain (Andalusia) for the period 1990–99. The WRF configuration consisted of a 10-km-resolution domain nested in a coarser domain driven by 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data, with spectral nudging above the PBL employed over the latter domain. The model outputs have been compared at different time scales with an observational dataset that comprises 438 rain gauges and 152 temperature stations with records of both daily maximum and minimum temperatures. To reduce the “representation error,” the validation with observations has been performed using a multistep regionalization that distinguishes five precipitation regions and four temperature regions. The analysis proves that both cumulus and PBL schemes have a crucial impact on the description of precipitation in Andalusia, whereas no noticeable differences between microphysics options were appreciated. Temperature is described similarly by all the configurations, except for the PBL choice affecting minimum values. WRF provides a definite improvement over ERA-40 in the climate description in terms of frequency, spatial distribution, and intensity of extreme events. It also captures more accurately the annual cycle and reduces the biases and the RMSE for monthly precipitation, whereas only a minor enhancement of these features was obtained for monthly-mean maximum and minimum temperatures. The results indicate that WRF is able to correctly reproduce Andalusian climate and produces useful added-value information for climate studies.

Summary This paper investigates the extreme precipitation events in the southern Iberian Peninsula (Andalusia) using 86 stations with daily precipitation records for the period 1955–2006. Seasonal (excluding summer) and monthly trends were established for different extreme-precipitation indices. Both the stability and the significance of the trends were examined. The findings indicate a significant spatial and seasonal variability. The most noteworthy results were found in winter, with a predominance of decreasing trends for intensity indices in western and central (CW) Andalusia, whereas positive trends appeared in the south-eastern (SE) zone. These marked differences in the sign of trends in short distances evidence the importance of using a dense network of stations for regional analysis of extremes. Overall, spring analysis reveals a negative trend in the intensity indices, especially in March. In autumn, the significance of the trends proved lower in comparison to winter and spring, and only September presented significant increasing trends in intensity indices. The synoptic patterns associated with heavy precipitation events were compared with two large-circulation indices affecting the precipitation over Andalusia (the North Atlantic Oscillation, NAO, and the Western Mediterranean Oscillation, WeMO). This analysis was made for the two sub-areas of Andalusia separately. The synoptic patterns at the surface and 500-hPa level were determined by the use of a principal component analysis (PCA) in T-mode coupled with clustering techniques. The seasonal distribution and the location of disturbances of the atmospheric patterns found were clearly differentiated in the two areas. The tendencies found for the indices agree with the changes in the frequency of the main synoptic patterns for each area. Furthermore, most of these synoptic patterns were related to highly negative WeMO values. Conversely, only some of the patterns associated with heavy rainfall in the western and central area presented significant negative values of the NAO index.

The Australian eastern seaboard is a distinct climate entity from the interior of the continent, with different climatic influences on each side of the Great Dividing Range. Therefore, it is plausible that downscaling of global climate models could reveal meaningful regional detail, or ‘added value’, in the climate change signal of mean rainfall change in eastern Australia un-der future scenarios. However, because downscaling is typically done using a limited set of global climate models and downscaling methods, the results from a downscaling study may not represent the range of uncertainty in plausible projected change for a region suggested by the ensemble of host global climate models. A complete and unbiased representation of the plausible changes in the climate is essential in producing climate projections useful for future planning. As part of this aim it is important to quantify any differences in the change signal between global climate models and downscaling, and understand the cause of these differ-ences in terms of plausible added regional detail in the climate change signal, the impact of sub-sampling global climate models and the effect of the downscaling models themselves. Here we examine rainfall projections in eastern Australia under a high emissions scenario by late in the century from ensembles of global climate models, two dynamical downscaling models and one statistical downscaling model. We find no cases where all three downscaling methods show the same clear regional spatial detail in the change signal that is distinct from the host models. However, some downscaled projections suggest that the eastern seaboard could see little change in spring rainfall, in contrast to the substantial rainfall decrease inland. The change signal in the downscaled outputs is broadly similar at the large scale in the various model outputs, with a few notable exceptions. For example, the model median from dynamical downscaling projects a rainfall increase over the entirety of eastern Australia in autumn that is greater than the global models. Also, there are some instances where a downscaling method produces changes outside the range of host models over eastern Australia as a whole, thus ex-panding the projected range of uncertainty. Results are particularly uncertain for summer, where no two downscaling studies clearly agree. There are also some confounding factors from the model configuration used in downscaling, where the particular zones used for statis-tical models and the model components used in dynamical models have an influence on results and produce additional uncertainty.

[1] A set of eight simulations at high spatial resolution (10 km) were performed with WRF to generate future projections of mean and extreme precipitation over Spain. The model was driven with two GCMs (ECHAM5 and CCSM3.0) under the conditions of three SRES emissions scenarios (B1, A1B and A2) which amounts to six future (2070–2099) climate simulations. Two present (1970–1999) climate simulations forced by the same GCMs were previously completed and were used as a baseline to allow for comparison and quantify the changes. The annual and seasonal precipitation means were examined to elucidate how global warming will manifest in the future precipitation climatic means. The distribution of precipitation in different-intensity events and the percentiles were calculated to further describe the nature of the changes. Additionally, a number of extreme indices were explored to determine changes in the low frequency events and the persistency of specific conditions. The results indicate that Spain might be exposed to a substantial decrease in annual precipitation that range between −18% and −42% depending on the simulation, with a particularly severe reduction during the summer, between −32% and −72%. These changes are generally caused by less light-to-moderate events, which leads to a displacement of the probability density function toward higher values. Namely, future climate might be characterized by less precipitation but more concentrated in extreme events, although their occurrence in absolute terms might not vary. The projections also suggest that, by the end of the century, the wet periods might be shorter and the dry spells significantly longer.

The advection–diffusion equations defining control volume conservation laws in micrometeorological research are analysed to resolve discrepancies in their appropriate scalar variables for heat and mass transport. A scalar variable that is conserved during vertical motions enables the interpretation of turbulent mixing as ‘diffusion’. Gas-phase heat advection is shown to depend on gradients in the potential temperature (θ), not the temperature (T). Since conduction and radiation depend on T, advection–diffusion of heat depends on gradients of both θ and T. Conservation of θ (the first Law of Thermodynamics) requires including a pressure covariance term in the definition of the turbulent heat flux. Mass advection and diffusion are universally agreed to depend directly on gradients in the gas ‘concentration’ (c), a nonetheless ambiguous term. Depending upon author, c may be defined either as a dimensionless proportion or as a dimensional density, with non-trivial differences for the gas phase. Analyses of atmospheric law, scalar conservation and similarity theory demonstrate that mass advection–diffusion in gases depends on gradients, not in density but rather in a conserved proportion. Flux-tower researchers are encouraged to respect the meteorological tradition of writing conservation equations in terms of scalar variables that are conserved through simple air motions., The authors received funding support from Andalusian regional government project GEOCARBO (P08-RNM-3721), the National Institute for Agrarian Research and Technology (INIA; SUM2006–00010-00–00), the Spanish flux-tower network CARBORED-ES (Science Ministry project CGL2010- 22193-C04–02), and the European Commission collaborative project GHG Europe (FP7/2007-2013; grant agreement 244122).

We examine the joint contribution of urban expansion and climate change on heat stress over the Sydney region. A Regional Climate Model was used to downscale present (1990-2009) and future (2040-2059) simulations from a Global Climate Model. The effects of urban surfaces on local temperature and vapor pressure were included. The role of urban expansion in modulating the climate change signal at local scales was investigated using a human heat-stress index combining temperature and vapor pressure. Urban expansion and climate change leads to increased risk of heat-stress conditions in the Sydney region, with substantially more frequent adverse conditions in urban areas. Impacts are particularly obvious in extreme values; daytime heat-stress impacts are more noticeable in the higher percentiles than in the mean values and the impact at night is more obvious in the lower percentiles than in the mean. Urban expansion enhances heat-stress increases due to climate change at night, but partly compensates its effects during the day. These differences are due to a stronger contribution from vapor pressure deficit during the day and from temperature increases during the night induced by urban surfaces. Our results highlight the inappropriateness of assessing human comfort determined using temperature changes alone and point to the likelihood that impacts of climate change assessed using models that lack urban surfaces probably underestimate future changes in terms of human comfort.

The Maritime Continent is the largest archipelago in the world and a region of intense convective activity that influences Earth’s general circulation. The region features one of the warmest oceans, very complex topography, dense vegetation, and an intricate configuration of islands, which together result in very specific precipitation characteristics, such as a marked diurnal cycle. Atmospheric models poorly resolve deep convection processes that generate rainfall in the archipelago and show fundamental errors in simulating precipitation. Spatial resolution and the use of convective schemes required to represent subgrid convective circulations have been pointed out as culprits of these errors. However, models running at the kilometer scale explicitly resolve most convective systems and thus are expected to contribute to solve the challenge of accurately simulating rainfall in the Maritime Continent. Here we investigate the differences in simulated precipitation characteristics for different representations of convection, including parameterized and explicit, and at various spatial resolutions. We also explore the vertical structure of the atmosphere in search of physical mechanisms that explain the main differences identified in the rainfall fields across model experiments. Our results indicate that both increased resolution and representing convection explicitly are required to produce a more realistic simulation of precipitation features, such as a correct diurnal cycle both over land and ocean. We found that the structures of deep and shallow clouds are the main differences across experiments and thus they are responsible for differences in the timing and spatial distribution of rainfall patterns in the various convection representation experiments.

Antecedent land surface conditions play a role in the amplification of temperature anomalies experienced during heatwaves by modifying the local partitioning of available energy between sensible and latent heating. Most existing analyses of heatwave amplification from soil moisture anomalies have focused on exceptionally rare events and consider seasonal scale timescales. However, it is not known how much the daily evolution of land surface conditions, both before and during a heatwave, contributes to the intensity and frequency of these extremes. We examine how the daily evolution of land surface conditions preceding a heatwave event contributes to heatwave intensity. We also diagnose why the land surface contribution to Australian heatwaves is not homogeneous due to spatiotemporal variations in land‐atmosphere coupling. We identify two coupling regimes: a land‐driven regime where surface temperatures are sensitive to local variations in sensible heating and an atmosphere‐driven regime where this is not the case. Northern Australia is consistently strongly coupled, where antecedent soil moisture conditions can influence temperature anomalies up to day 4 of a heatwave. For southern Australia, heatwave temperature anomalies are not influenced by antecedent soil moisture conditions due to an atmosphere‐driven coupling regime. Therefore, antecedent land surface conditions have a role in increasing the temperature anomalies experienced during a heatwave only over regions with strong land‐driven coupling. The timescales over which antecedent land surface conditions contribute to Australian heatwaves also vary regionally. Overall, the spatiotemporal variations of land‐atmosphere interactions help determine where and when antecedent land surface conditions contribute to Australian heat extremes.