<p>Quantifying the impact of climate change on actual and potential evapotranspiration (AET and PET) is essential for water security, agriculture production and environmental management. AET and PET are strongly influenced by local factors such as topography, land cover and soil moisture, which limits the usability of global climate models for their projections. Here, we dynamically downscale Coupled Model Intercomparison Project Phase 6 (CMIP6) models using Conformal Cubic Atmospheric Model (CCAM) to a 10km resolution over Australia and derive AET and PET at a daily time step using the Morton method and project future changes under SSP126, 245 and 370. Three AET / PET datasets are provided by Queensland Government Climate Projection Service team, which include Areal AET, Wet Environment Areal PET and Point PET. These datasets are computed offline based on Morton&rsquo;s Complementary Relationship Areal Evapotranspiration (CRAE) model.</p> <p>In addition, we also provide datasets for Pan Evaporation (linear regression model), Short and Tall Crop Reference Evapotranspiration (Penman&ndash;Monteith model) and Shallow Lake Evaporation (Morton&rsquo;s Complementary Relationship Wet-surface Evaporation CRWE model). They have used dynamically downscaled CMIP6 models datasets as input.</p>

Schools Weather and Air Quality (SWAQ) is a citizen science project funded by the Department of Industry, Innovation and Science as part of its Inspiring Australia - Citizen Engagement Program. SWAQ is equipping public schools across Sydney with research-grade meteorology and air quality sensors, enabling students to collect and analyse research quality data through curriculum-aligned classroom activities. The network includes twelve automatic weather stations and seven automatic air quality stations, stretched from -33.5995° to -34.0421° latitude and from 150.6913° to 151.2708° longitude. The average spacing is 10.2 km and the average installation height is 2.5 m above ground level. Optimum site allocation was determined by undertaking a multi-criteria weighted overlay analysis to ensure data representativeness and quality. Six meteorological parameters (dry-bulb temperature, relative humidity, barometric pressure, rain, wind speed, and wind direction) and six air pollutants (SO2, NO2, CO, O3, PM2.5, and PM10) are recorded. Observations and metadata are available from September 2019 for WXT536 + AQT420 stations and from October 2019 for WXT536 stations (refer to Table 1 of the Dataset Guide), thus encompassing the Black Summer bushfire and the COVID-19 lockdown period. Data routinely undergo quality control, quality assurance and publication.