- 1Mitiga Solutions SL, Barcelona, Spain (mirta.pinilla@mitigasolutions.com)
- 2Computer Architecture and Operating Systems Department, Universitat Autonoma de Barcelona, Bellaterra, Spain (marc.benitezb@autonoma.cat)
Wildfires pose a growing threat to populated areas of the Mediterranean basin. The hot and dry conditions caused by climate change have exacerbated the risk, extent, and severity of wildfires. The Barcelona Metropolitan Area, a large metropolis with an extended wildland-urban interface (WUI), is particularly vulnerable.
Assessment of the impact of climate change on heat and droughts, and the cascading effects on future wildfire risk in WUI areas under different climate scenarios requires future projections of temperature and precipitation data. Current spatial resolution in standard climate projections is approximately 100km, insufficient to properly assess the spatial and temporal variability in heatwaves and drought conditions. Climate information at a much finer spatial scale is required to properly assess future climate risk at a metropolitan scale.
To obtain km-scale future climate data we train a U-Net using two inputs: ERA5, and an elevation map (Copernicus DEM GLO-90), using as a target dataset the CHELSA Global reanalysis (https://www.chelsa-climate.org/)). The U-Net neural network learns the relationship between coarser resolution predictors (from ERA5 at 0.25 deg, ~25 km) and the high-resolution predicted variables (from CHELSA at 30", ~0.8 km) over the training domain. The trained U-Net is then used to infer the high-resolution surface variables (maximum and minimum daily air temperature and daily precipitation at 30”) from the coarser resolution CMIP6 future climate projections, bias corrected and statistically downscaled to 0.25 deg (obtained from the Global Downscaled Projections for Climate Impacts Research dataset).
We validate our results against meteorological stations in Catalonia during the historical period and find that biases and RMSE are smaller than the coarser-resolution climate data. Furthermore, the temporal trends of the downscaled climate data are preserved and identical to the original climate model trends.
Our results demonstrate that the proposed methodology is robust to provide high-resolution heat and drought indicators.
How to cite: Rodriguez Pinilla, M., Benitez Benavides, M., Exarchou, E., Margalef, T., and Panadero, J.: Deep Learning Downscaling of Precipitation and Temperature Climate Data for Future Wildfire Risk Assessment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12404, https://doi.org/10.5194/egusphere-egu26-12404, 2026.
