This dataset contains output data from the WRF-Comfort weather model for the REFERENCE (BASE) simulations and the COOL ROOFS adaptation strategy for the current period (2018), mid-century (2050) and end-of-century (2100). Within each wrfout file, multiple weather variables can be analyzed.

METHODOLOGICAL INFORMATION

This study assesses the cooling potential of three heat adaptation strategies for the AMB under the SSP3–7.0 future climate scenario. Using the PGW approach, the 2018 HW episode is projected to 2050 and 2100 to evaluate temperature and thermal stress reductions and vulnerability mitigation. For the workflow, three key datasets are used for modeling and downscaling: (a) high-resolution land use data with 26 categories of built and green areas and urban morphology characteristics, provided by the Urban Planning Office of the AMB; (b) meteorological data for the WRF modeling from ERA5, a reanalysis dataset from the European Centre for Medium-Range Weather Forecasts with a 31 km resolution, 6-h intervals, and 37 vertical pressure levels; (c) future climate simulations from the Community Earth System Model 2 (CESM2) Large Ensemble (LENS2). LENS2 consists of 100 members at a 1◦ resolution, covering from 1850 to 2100 under the CMIP6 historical and the SSP3–7.0 radiative forcing scenario, which is defined as a moderate, business-as-usual scenario. The LENS2 dataset considers both oceanic and atmospheric processes, allowing for the computing of climate fluctuations generated by greenhouse warming.

1. Description of methods used for collection-generation of data: Pseudo Global Warming: Dai, A., Rasmussen, R.M., Ikeda, K., Liu, C., 2020. A new approach to construct representative future forcing data for dynamic downscaling. Clim. Dyn. 55 (1–2), 315–323. https://doi.org/10.1007/s00382-017-3708-8. Modeling with WRF BEP+BEM: Ventura, S., Miro, J.R., Segura-Barrero, R., Chen, F., Martilli, A., Liu, C., Ikeda, K., Villalba, G., 2025. Assessing the Intensity of Heatwaves in a Warming Climate at the Urban Scale: A Case Study of the Metropolitan Area of Barcelona. Journal of Geophysical Research: Atmospheres 130, e2025JD043559. https://doi.org/10.1029/2025JD043559

2. Methods for processing the data: It has been modeled with the Weather Research and Forecasting (WRF) model thanks to the computational resources of the Red Española de Supercomputación (RES). Python has been mainly used to process the data.

3. Instrument- or software- specific information needed to interpret the data: To interpret the data, you can use Python, extract the necessary variables and plot what is required, or GIS software (QGIS, ARCGIS).

4. Instruments, calibration and standards information: To validate the simulations, data from automatic stations from the Servei Meteorològic de Catalunya was used.

5. Environmental or experimental conditions: N/A

6. Quality-assurance procedures performed on the data: It has been validated with data from stations of the Servei Meteorològic de Catalunya using correlation coefficients (R), Mean Bias (MB) and root-mean-square error (RMSE).