In 2022, the observed annual average mean temperature in Australia reached 21.96 degrees Celsius. Overall, the annual average temperature had increased compared to the temperature reported for 1901. Impact of climate change The rising temperatures in Australia are a prime example of global climate change. As a dry country, peak temperatures and drought pose significant environmental threats to Australia, leading to water shortages and an increase in bushfires. Western and South Australia reported the highest temperatures measured in the country, with record high temperatures of over 50°C in 2022. Australia’s emission sources While Australia has pledged its commitment to the Paris Climate Agreement, it still relies economically on a few high greenhouse gas emitting sectors, such as the mining and energy sectors. Australia’s current leading source of greenhouse gas emissions is the generation of electricity, and black coal is still a dominant source for its total energy production. One of the future challenges of the country will thus be to find a balance between economic security and the mitigation of environmental impact.

The CSIRO versions of the BOM (Bureau of Meteorology Australia) Australian Temperature datasets are a concatenation of the individual monthly Temperature datasets into a single contiguous netcdf file for the time period 1900-2007 with a spatial resolution of .25° x .25°. The variables tav(Average Temperature), tmax (Maximum Temperature), tmin (Minimum Temperature) & tdtr (Diurnal Temperature Range) are available for the whole of Australia and also as a subset for the Murray Darling Basin. These have also been processed to include calculated Anomaly, Climatology, and Seasonal datasets available for Australia. There are approximately 17 files for Temperature data totalling 921.32 MB.

(From http://www.worldclim.org/methods) - For a complete description, see:

Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as 1 km2 resolution). Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables.

The WorldClim interpolated climate layers were made using: * Major climate databases compiled by the Global Historical Climatology Network (GHCN), the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet, and a number of additional minor databases for Australia, New Zealand, the Nordic European Countries, Ecuador, Peru, Bolivia, among others. * The SRTM elevation database (aggregeated to 30 arc-seconds, 1 km) * The ANUSPLIN software. ANUSPLIN is a program for interpolating noisy multi-variate data using thin plate smoothing splines. We used latitude, longitude, and elevation as independent variables.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of precipitation across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using trivariate splines (latitude, longitude and elevation as spline variables) using a DEM smoothed (Gaussian filter with a standard deviation of 10 and a search radius of 0.0825°, optimised using cross validation) to account for the lack of strong correlation between elevation and precipitation at short distances (Hutchinson 1998; Sharples et al. 2005). All data was interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Monthly surfaces were interpolated directly from monthly station records using the methods described in step 3. 5. Daily anomalies were calculated as a proportion of monthly precipitation, and interpolated with full spline dependence on latitude and longitude. 6. Interpolated anomalies (constrained to values between 0 and 1) were multiplied by monthly precipitation to obtain the final daily surfaces. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. C) Model performance: Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly precipitation: RMSE = 7.65 mm (14.0% relative to mean) Monthly precipitation: RMSE = 13.12 mm (24.7% relative to mean) Daily precipitation: RMSE = 2.21 mm (26.3% relative to mean)

In 2023, the annual mean rainfall in Australia was 473.7 millimeters. Over the last twenty years, the mean area-average rainfall has fluctuated in Australia, with the lowest value recorded in 2019.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Various climate variables summary for all 15 subregions based on Bureau of Meteorology Australian Water Availability Project (BAWAP) climate grids. Including

1. Time series mean annual BAWAP rainfall from 1900 - 2012.

2. Long term average BAWAP rainfall and Penman Potentail Evapotranspiration (PET) from Jan 1981 - Dec 2012 for each month

3. Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P (precipitation); (ii) Penman ETp; (iii) Tavg (average temperature); (iv) Tmax (maximum temperature); (v) Tmin (minimum temperature); (vi) VPD (Vapour Pressure Deficit); (vii) Rn (net radiation); and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend.

4. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009).

As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

There are 4 csv files here:

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset History

Dataset was created from various BAWAP source data, including Monthly BAWAP rainfall, Tmax, Tmin, VPD, etc, and other source data including monthly Penman PET, Correlation coefficient data. Data were extracted from national datasets for the GLO subregion.

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset Citation

Bioregional Assessment Programme (2014) GLO climate data stats summary. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/afed85e0-7819-493d-a847-ec00a318e657.

Dataset Ancestors

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

Mean monthly and mean annual 9am and 3pm relative humidity grids. The grids show the relative humidity values across Australia in the form of two-dimensional array data. The mean data are based on …Show full descriptionMean monthly and mean annual 9am and 3pm relative humidity grids. The grids show the relative humidity values across Australia in the form of two-dimensional array data. The mean data are based on the standard 30-year period 1976-2005. Relative Humidity (RH) data are recorded at a network of weather stations across Australia. The data from these weather stations are stored electronically in the Bureau of Meteorology's database called ADAM (Australian Data Archive for Meteorology). The average RH maps were based on daily 9am and 3pm RH data from about 530 weather stations. The daily data were extracted from ADAM and a number of quality control checks were then applied to the data. After the quality checks were applied to the data, monthly and annual 9am and 3pm averages were calculated for each of the weather stations. An objective analysis technique was then applied to these station averages to produce regular gridded datasets (each month and annual) covering Australia.

Three datasets containing climate data, compiled in April 2011, have been purchased from the Bureau of Meteorology. These datasets include observations from stations in all Australian States and Territories. Each dataset includes a file which gives details of the stations where observations were made and a file describing the data. AWS Hourly Data contains hourly records of precipitation, air temperature, wet bulb temperature, dew point temperature, relative humidity, vapour pressure, saturated vapour pressure, wind speed, wind direction, maximum wind gust, mean sea level pressure, station level pressure. Each record for each parameter is also flagged to indicate the quality of the value.Synoptic Data contains records of air temperature, dew point temperature, wet bulb temperature, relative humidity, wind speed, wind direction, mean sea level pressure, station level pressure, QNH pressure, vapour pressure and saturated vapour pressure. Each record for each parameter is also flagged to indicate the quality of the value.Daily Rainfall Data contains records precipitation in the 24 hours before 9 am, number of days of rain within the days of accumulation and the accumulated number of days over which the precipitation was measured. Each precipitation record is flagged to indicate the quality of the value.

Australian Bureau of Meteorology assembled this dataset of 191 Australian rainfall stations for the purpose of climate change monitoring and assessment. These stations were selected because they are believed to be the highest quality and most reliable long-term rainfall stations in Australia. The longest period of record is August 1840 to December 1990, but the actual periods vary by individual station. Each data record in the dataset contains at least a monthly precipitation total, and most records also have daily data as well.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of minimum temperature (approx. 1.2 m from ground) across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Surfaces are developed using trivariate splines (latitude, longitude and elevation) with partial dependence upon a topographic index of relative elevation. Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using trivariate splines (latitude, longitude and elevation as spline variables) with partial dependence upon a topographic index of relative elevation. All models were fit and interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Daily anomalies were calculated by subtracting daily observations from climate normals and interpolated with full spline dependence upon latitude and longitude 5. Interpolated anomalies were added to interpolated climate normals to obtain the final daily surfaces. 6. Monthly surfaces are calculated as an aggregation of the daily product. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. C) Model performance (3DS-T): Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly minimum temperature RMSE = 0.80 °C Daily minimum temperature RMSE = 1.73 °C

Please refer to the linked manuscript for further details.

This statistic displays the monthly mean rainfall in Melbourne, Australia, between 1855 and 2016. According to the source, 58 millimeters of rain fell on average in Melbourne in April.

This dataset contains time series for monthly precipitation over six sites (Blackheath, Braidwood, Darkes Forest, Goulburn, Lithgow and Moss Vale) in the Sydney Catchment Area (SCA) and monthly mean maximum and mean minimum temperature for three sites (Goulburn, Lithgow, and Moss Vale) in the SCA. This data was used in the study Attribution and Prediction of Precipitation and Temperature Trends within the Sydney Catchment Using Machine Learning. The data was originally from the Australian Bureau of Meteorology Climate Data Online (http://www.bom.gov.au/climate/data/index.shtml), but has been updated to have missing values (8% of data) filled using a moving average centred on the year for which the data is missing.

Below is the abstract for the paper:

Droughts in southeastern Australia can profoundly affect the water supply to Sydney, Australia's largest city. Increasing population, a warming climate, land surface changes, and expanded agricultural use increase water demand and reduce catchment runoff. Studying Sydney's water supply is necessary to manage water resources and lower the risk of severe water shortages. This study aims at understanding Sydney water supply by analysing precipitation and temperature trends across the catchment. A decreasing trend in annual precipitation was found across the Sydney catchment area. Annual precipitation also is significantly less variable, due to fewer years above the 80th percentile. These trends result from significant reductions in precipitation during spring and autumn, especially over the last 20 years. Wavelet analysis is applied to assess how the influence of climate drivers has changed over time. Attribute selection was carried out using linear regression and machine learning techniques including random forests and support vector regression. Drivers of annual precipitation included Niño3.4, SAM, DMI and measures of global warming such as the Tasman Sea Sea Surface temperature anomalies. The support vector regression model with a polynomial kernel achieved correlations of 0.921 and a skill score compared to climatology of 0.721. The linear regression model also performed well with a correlation of 0.815 and skill score of 0.567, highlighting the importance of considering both linear and non-linear methods when developing statistical models. Models were also developed on autumn and winter precipitation but performed worse than annual precipitation on prediction. For example, the best performing model on autumn precipitation, which accounts for approximately one quarter of annual precipitation, achieved an RMSE of 418.036 mm2 on the testing data while annual precipitation achieved an RMSE of 613.704 mm2. However, the seasonal models provided valuable insight into whether the season would be wet or dry compared to the climatology.

This weather data package comprises weather data for automatic weather stations situated at 13 sites separated by distances of between 5 and 80 km. The weather stations record temperature and rainfall (in 2010, one weather station was set up so that it also began recording wind speed and direction). The air temperature, rainfall, wind speed and wind direction data are recorded in a data logger housed within the instrument stand. The network program uses a core of 12 sites and aims to quantitatively track long-term shifts in biodiversity and ecological processes in relation to key drivers, including unpredictable rainfall and droughts, fire, feral predators and grazing. A synopsis of related data packages which have been collected as part of the Desert Ecology Plot Network's full program is provided at http://www.ltern.org.au/index.php/ltern-plot-networks/desert-ecology

This statistic displays the monthly mean rainfall in Perth, Australia, between 1993 and 2016. According to the source, 41 millimeters of rain fell on average in Perth in April.

Abstract The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets provided by the Bureau of Meteorology. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived. This dataset includes the following parameters for the whole of Australia: Mean annual BAWAP (Bureau of Meteorology Australian Water Availability Project) …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets provided by the Bureau of Meteorology. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived. This dataset includes the following parameters for the whole of Australia: Mean annual BAWAP (Bureau of Meteorology Australian Water Availability Project) rainfall of year 1981 - 2013 Mean annual penman PET (potential evapotranspiration) of year 1981 - 2013 Mean annual runoff using the 'Budyko-framework' implementation of Choudhury Purpose Provide long term (last 30 years) average annual grids of rainfall, penman PET and runoff for whole Australia. Dataset History The mean annual rainfall data is created from monthly BAWAP grids (Dataset ID: 7aaf0621-a0e5-4b01-9333-53ebcb1f1c14) which is created from daily BILO rainfall. Jones, D. A., W. Wang and R. Fawcett (2009). "High-quality spatial climate data-sets for Australia." Australian Meteorological and Oceanographic Journal 58(4): 233-248. The Mean annual penman PET is created by Randall Donohue, as per the Donohue et al (2010) paper using the fully physically based Penman formulation of potential evapotranspiration, except that daily wind speed grids used here were generated with a spline (i.e., ANUSPLIN) as per McVicar et al (2008), not the TIN as per Donohue et al (2010). For comprehensive details regarding the generation of some of these datasets (i.e., net radiation, Rn) see the details provided in Donohue et al (2009). Donohue, R.J., McVicar, T.R. and Roderick, M.L. (2010) Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology. 386(1-4), 186-197. doi:10.1016/j.jhydrol.2010.03.020 Donohue, R.J., McVicar, T.R. and Roderick, M.L., (2009) Generating Australian potential evaporation data suitable for assessing the dynamics in evaporative demand within a changing climate. CSIRO: Water for a Healthy Country Flagship, pp 43. http://www.clw.csiro.au/publications/waterforahealthycountry/2009/wfhc-evaporative-demand-dynamics.pdf McVicar, T.R., Van Niel, T.G., Li, L.T., Roderick, M.L., Rayner, D.P., Ricciardulli, L. and Donohue, R.J. (2008) Wind speed climatology and trends for Australia, 1975-2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophysical Research Letters. 35, L20403, doi:10.1029/2008GL035627 The Mean annual runoff was created by Randall Donohue, as per the Donohue et al (2010) paper. The data represent the runoff expected from the steady-state 'Budyko curve' longterm mean annual water-energy limit approach using BAWAP precipitation and the Penman potential ET described above. Choudhury BJ (1999) Evaluation of an empirical equation for annual evaporation using field observations and results from a biophysical model. Journal of Hydrology 216, 99-110. Donohue, R.J., McVicar, T.R. and Roderick, M.L. (2010) Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology. 386(1-4), 186-197. doi:10.1016/j.jhydrol.2010.03.020 Donohue, R.J., McVicar, T.R. and Roderick, M.L., (2009) Generating Australian potential evaporation data suitable for assessing the dynamics in evaporative demand within a changing climate. CSIRO: Water for a Healthy Country Flagship, pp 43. http://www.clw.csiro.au/publications/waterforahealthycountry/2009/wfhc-evaporative-demand-dynamics.pdf McVicar, T.R., Van Niel, T.G., Li, L.T., Roderick, M.L., Rayner, D.P., Ricciardulli, L. and Donohue, R.J. (2008) Wind speed climatology and trends for Australia, 1975-2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophysical Research Letters. 35, L20403, doi:10.1029/2008GL035627 Dataset Citation Bioregional Assessment Programme (2014) Mean Annual Climate Data of Australia 1981 to 2012. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/02418c67-f8bb-48a8-88a3-2a5c6b485f78. Dataset Ancestors Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

Monthly application ready data at 5km grid resolution for Australia for 2 emissions scenarios (RCP4.5 an RCP8.5), 8 CMIP5 GCMs, for 4 time periods, each of 30 years centred on 2030, 2050, 2070 and 2090. Temperatures are mean scaled and rainfall decile scaled against historical observationally derived gridded data sets. Lineage: Bilinear interpolation from GCM grid to 5km grid.

This statistic displays the monthly mean rainfall in Sydney, Australia, between 1929 and 2016. According to the source, 106 millimeters of rain fell on average in Sydney in April.

This statistic displays the monthly mean rainfall in Darwin, Australia, between 1941 and 2016. According to the source, 102 millimeters of rain fell on average in Darwin in April.

Changes in ocean temperature are of immense importance for global climate studies, marine ecology and fish recruitment, as well as being of interest to recreational swimmers. Because of the Leeuwin Current, water temperatures off Western Australia are some 4°C warmer than those at corresponding latitudes off the west coasts of southern Africa and South America. Water temperature measurements along the Western Australian coast are relatively sparse, although some intensive studies have been undertaken near cities and regional centres, as well as at selected sites important for fisheries.

Monthly temperature statistics have been compiled for a number of sites along the west coast of Australia, using a variety of data sources. To view monthly average temperature and minimum and maximum recorded at any site, click on the appropriate places on the map via the URL.

Annual and seasonal rainfall data for selected locations and three study areas was produced to assist identifying statistically significant long-term trends between 1907 and 2018. The selected weather stations represent different climate and rainfall zones in each study area. The objective is to compare year-to-year variability in precipitation with any potential long-term trend and to understand if recent experience of a drying trend in parts of Australia are part of a longer-term trend. Lineage: Station and regional data for seasonal and annual precipitation is calculated from monthly precipitation data. Summer precipitation is defined as the sum of monthly precipitation in December and January and February of the following year. For example, summer precipitation in 2019 relates to the summer 2019/20. Winter precipitation is defined as the sum of monthly precipitation in June, July and August. Regional data is calculated from first calculating annual and seasonal precipitation as described above for every raster grid cell in the study area and then averaging across the whole study area.

Selected weather stations and Bureau of Meteorology station IDs are: Broomehill (WA) -10525, Merredin (WA) - 10092, Mingenew (WA) - 08088, Bingara (NSW) - 54004, Cunnamulla (QLD) - 44026, Curlewis (Pine Cliff) (NSW) - 55045, Miles (QLD) - 42023, Narrabri (NSW) - 53026, Peak Hill (NSW) - 50031, Pittsworth (QLD) - 41082, Wallangra (QLD) - 54036, Clarence Town (NSW) - 61010, Murgon (QLD) - 40152, Yamba (NSW) - 58012