Heatwave frequency
Key messages
- Heatwave frequency is projected to increase across NSW by the middle of the century under all emissions scenarios.
- Under a medium-emissions (SSP2-4.5) and high-emissions (SSP3-7.0) scenario heatwave frequency will increase to the end of the century.
- Under a low-emissions (SSP1-2.6) scenario, although heatwave frequency still increases, the rate at which it is increasing is projected to slow down towards the end of the century, highlighting the importance of reducing emissions.
- The highest heatwave frequency is expected in the north-east of the state.
- The higher the heatwave frequency, the greater the implications for human health, agriculture, and infrastructure.
- Heatwave frequency projections can be used to assess how often populations and ecosystems are exposed to extreme heat events. This supports adaptation planning and the development of risk management strategies.
Heatwave background
What is heatwave frequency and how is it measured?
Heatwave frequency, or HWF, is the number of days each year that contribute to a heatwave event. For example, if there are two heatwave events in a year that each last three days, the HWF would be six days for that year.
A heatwave is determined using the excess heat factor (EHF)1 which considers:
- Comparison of the average temperatures for a 3-day period against the hottest days on record at that location with respect to the annual temperature threshold at the location (above the 95th percentile).
- The observed temperatures at that location over the past 30 days.
Key findings
Changes to heatwave frequency
Heatwave frequency is projected to increase across NSW by the middle of the century under all climate models and emissions scenarios when compared to the baseline periodi (1990–2009). In the second half of the century, heatwave frequency continues to increase under a medium-emissions (SSP2-4.5) and high-emissions (SSP3-7.0) scenario, while under a low-emissions scenario (SSP1-2.6), that rate of increase slows resulting in only a minor increase by the end of the century when compared to mid-century (Figure 1).
Figure 2. Projected change in average annual number of heatwave frequency (days) under a low-, medium- and high-emissions scenario for the time periods 2020–2039 (near-future), 2040–2059 (mid-century), 2060–2079 (late century) and 2080–2099 (end of century). Change projections are relative to historical baseline 1990–2009.
By the middle of the centuryii
- The frequency of heatwave days is projected to be more than double the baseline frequency under all emissions scenarios.
- The number of annual heatwave days across the state could increase by over 22 days from the baseline under a high-emission scenario.
By the end of the centuryiii:
- The state could see an average mean of 23.9 heatwave days per year under a low-emissions scenario, an average of 42.4 heatwave days per year under a medium-emissions scenario and an average of 60.3 heatwave days per year under a high-emissions scenario.
- Under a high-emissions scenario the number of heatwave days across the state could increase by 49.7 days above the baseline period.
Heatwave frequency geographic differences across NSW
Average heatwave frequency will increase across the state. The greatest heatwave frequencies are expected in the north-east of the state, to the west of the Great Dividing Range.
Implications of increased heatwave frequency
A greater frequency of heatwaves increases the implications to human health, agriculture, and infrastructure. For example, instances of heat stress could increase for the public, and the health sector may be required to deal with more frequent heat-related illness.3 Crops and livestock will also be exposed to more heatwave days, which could impact production in the industry.4 A modelling study for Australian sheep estimates that 2.1 million potential lambs are lost annually due to heat stress alone, increasing to 3.3 million with 3°C warming over the historical baseline.5
Additionally, more frequent and intense heatwaves can expose infrastructure to temperatures beyond the operating conditions assumed in historical design and planning, placing stress on infrastructure that may have been designed for historical temperatures, and may result in increased wear and material degradation over time.6 Financial losses from the 2009 heatwave in southeast Australia have been estimated at $800 million, mainly due to power outages and disruptions to the transport system.7
Extended hot summer weather can cause significant demand on the electricity system, water services and the cost of living. This strain has increased over recent decades with the growing use of air conditioners in homes and businesses.8
Heatwaves have the biggest impact on the electricity grid during January and February, when increased cooling demand places additional pressure on the interconnected electricity system. If reserve capacity becomes limited and is compounded by generator or transmission outages, involuntary load shedding – the temporary disconnection of electricity supply to some customers to maintain system stability – may be required. This can result in controlled or rolling blackouts to maintain grid security.9
Uses of heatwave frequency climate data
Heatwave frequency projections can be used to assess how often populations and ecosystems are exposed to extreme heat events each year.
For example, information on heatwave frequency can be used by the health sector to plan for and prevent heat stress, while the agricultural sector may use the information to plan for impacts on crops and livestock. Information on increases to heatwave frequency can also inform urban planning, to ensure infrastructure is resilient to changing conditions.
NARCliM2.0 climate extreme indices
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Technical information
Heatwave indices using NARCliM2.0 are calculated over a 5-month austral summer period (November to March) per year, due to the high impacts of high temperatures are compared to expected summertime conditions. Heatwave indices are calculated using the Excess Heat Factor (EHF) method.
Excess heat factor (EHF) is based on a three-day-averaged daily mean temperature (DMT) and is intended to capture heatwave intensity as it applies to human health outcomes. The index is described and placed in a climatological context to derive heatwave severity.
EHF incorporates two ingredients. The first ingredient is a measure of how hot a three-day period (TDP) is with respect to an annual temperature threshold at each location. If the daily mean temperature averaged over the three-day period is higher than the climatological 95th percentile for DMT, then the TDP and each day within in it are deemed to be in heatwave conditions.
The second ingredient is a measure of how hot the TDP is with respect to the recent past (specifically the previous 30 days). This considers the idea that people acclimatise (at least to some extent) to their local climate, with respect to its temperature variation across latitude and throughout the year but may not be prepared for a sudden rise in temperature above that of the recent past.
Because of this definition, it is not enough to be ‘very hot’ to get a heatwave. It must be very hot compared to usual conditions at the given location, which means you can get heatwaves in cool alpine or coastal areas as well as in arid, inland areas. The EHF is calculated with reference to two different measures of temperature, so it has the unit of measure °C2. Once a heatwave is identified, the EHF provides information on the characteristics of such heatwaves.10
Description: The index heatwave frequency is the number of days in a year that contribute to heatwaves, as defined by Excess Heat Factor (EHF) and Heatwave Number (HWN).
Utility: HWF helps assess how often populations and ecosystems are exposed to extreme heat events in a year. It is useful for public health (heat-related illness prevention), agriculture (stress on livestock and crops), urban planning (heat-resilient infrastructure) and climate change monitoring (changes in extreme heat occurrence).
Units: Number of days
Frequency in NARCliM2.0: Yearly.
Time periods in this document
i. Baseline period: The modelled average for each climate variable from 1990 to 2009, used for comparison with future projections.
ii. Middle of the century: The projected annual average for 2040 to 2059. This is compared against a historical model baseline period. The projections for each time period represent averaged data across all 10 NARCliM climate models.
iii. End of the century: The projected annual average for 2080-2099. This is compared against a historical model baseline period. The projections for each time period represent averaged data across all 10 NARCliM climate models.
New South Wales and Australian Regional Climate Modelling (NARCliM)2.0 provides nation-leading climate model data that spans the range of plausible future changes in climate. It offers:
- climate projections to the year 2100, and simulations of the past
- 4-km scale projections for south-east Australia, 20-km scale projections for the broader Australasian region
- projections under low (SSP1-2.6), medium (SSP2-4.5), and high (SSP3-7.0) emissions scenarios to understand how climate risk differs depending on emissions pathways (Shared Socioeconomic Pathways, SSPs).
Further reading and information
- Nairn, J. & Fawcett, R (2015). ‘The excess heat factor: a metric for heatwave intensity and its use in classifying heatwave severity’, International journal of environmental research and public health. 12, 227-253 doi:10.3390/ijerph120100227
- World Health Organization, ‘Heat and health’, WHO website, 2024, accessed 21 August 2026.
- N Ghajarnia N, U Bende-Michl, W Sharples, E Carrara, S Tijs (2025) Evolving patterns of compound heat and water stress conditions: Implications for agriculture futures in Australia, Agricultural Water Management, Volume 316, 109573. doi:10.1016/j.agwat.2025.109573
- W Van Wettere, S Culley, A Swinbourne, S Leu, S Lee, A Weaver, J Kelly, S Walker, D Kleemann, D Thomas, P Hayman, K Gatford, K Kind and S Westra, (2024) ‘Heat stress from current and predicted increases in temperature impairs lambing rates and birth weights in the Australian sheep flock’, Nature Food, 5(3):206–210. doi:10.1038/s43016-024-00935-w
- National Climate Change Adaptation Research Facility (NCCARF), ‘Heat and heatwaves: Synthesis Summary’ 1, NCCARF, 2012, accessed 21 August 2026.
- E Mulholland and L Feyen. (2021) ‘Increased risk of extreme heat to European roads and railways with global warming’, Climate Risk Management, 34, 100365. doi:10.1016/j.crm.2021.100365. VHS. de Abreu, AS Santos, and TGM Monteiro. (2022) ‘Climate Change Impacts on the Road Transport Infrastructure: A Systematic Review on Adaptation Measures’. Sustainability, 14, 8864. doi:10.3390/su14148864
- Energy Networks Australia, ‘Factsheet: Heatwaves and electricity supply’ (PDF), Energy Networks Australia, 2019, accessed 20 August 2026.
- Peter Brodribb, Michael McCann, Graeme Dewerson, Jelena Franjić and Graham Anderson (2024) ‘Cold Hard Facts 4 – Key developments and emerging trends in the refrigeration and air conditioning industry in Australia’, report to Australian Government Department of Climate Change, Energy, the Environment and Water.
- Energy Networks Australia & Australian Energy Council (2020), Heatwaves and Electricity Supply Fact Sheet (PDF), Energy Networks Australia & Australian Energy Council, accessed 21 August 2026.
- Nairn, J. & Fawcett, R (2015). ‘The excess heat factor: a metric for heatwave intensity and its use in classifying heatwave severity’, International journal of environmental research and public health. 12, 227-253 doi:10.3390/ijerph120100227
Regional Climate Change Snapshots: The NARCliM2.0 projections are summarised as snapshots to provide accessible climate information that can support NSW communities to understand and plan for the impacts of climate change
Interactive Climate Change Projections Map: Select the region, climate variables and timescale in your area to explore what your region may look like in the future.
Climate Data Portal: The NSW Climate Data Portal variables dictionary provides technical descriptions and applications for each index
NSW Government, The NARCliM modelling methodology, Adapt NSW
NSW Government, NARCliM data processing, testing and validation, Adapt NSW