Urban greenery is a natural solution to cool cities and provide comfort, clean air and significant social, health and economic benefits. This paper aims to present the latest progress on the field of greenery urban mitigation techniques including aspects related to the theoretical and experimental assessment of the greenery cooling potential, the impact on urban vegetation on energy, health and comfort and the acquired knowledge on the best integration of the various types of greenery in the urban frame. Also to present the recent knowledge on the impact of climate change on the cooling performance of urban vegetation and investigate and analyse possible technological solutions to face the impact of high ambient temperatures.

We validated seasonal RayMan and ENVI-met mean radiant temperature (T_MRT) simulations to assess model performance in a sensitivity analysis from cold to extremely hot conditions. Human-biometeorological validation data were collected in Tempe, Arizona via transects during five field campaigns between 2014 and 2017. Transects were conducted across seven locations in two to three-hour intervals from 6:00 to 23:00 LST with a Kestrel meter and thermal camera (2014-2015) and the mobile instrument platform MaRTy (2017). Observations across diverse urban forms, sky view factors, and seasons covered a wide range of solar radiation regimes from a minimum T_MRT of 8.7 °C to a maximum of 84.9 °C. Both models produced large simulation errors across regimes with RMSE ranging from 8 °C to 12 °C (RayMan) and 11.2 °C to 16.1 °C (ENVI-met), exceeding a suggested T_MRT accuracy of ±5 °C for heat stress studies. RayMan model errors were largest for engineered enclosed spaces, complex urban forms, and extreme heat conditions. ENVI-met was unable to resolve intra-domain spatial variability of T_MRT and exhibited large errors with RMSE up to 25.5 °C for engineered shade. Both models failed to accurately simulate T_MRT for hot conditions. Errors varied seasonally with overestimated T_MRT in the summer and underestimated T_MRT in the winter and shoulder seasons. Results demonstrate that models should not be used under micrometeorological or morphological extremes without in-situ validation to quantify errors and assess directional bias due to model limitations.