  Climate models are essential for assessing the impact of greenhouse gas
emissions on our changing climate and the resulting increase in the frequency
and severity of natural disasters. Despite the widespread acceptance of climate
models produced by the Coupled Model Intercomparison Project (CMIP), they still
face challenges in accurately predicting climate extremes, which pose most
significant threats to both people and the environment. To address this
limitation and improve predictions of natural disaster risks, we introduce
Extreme Model Output Statistics (X-MOS). This approach utilizes deep regression
techniques to precisely map CMIP model outputs to real measurements obtained
from weather stations, which results in a more accurate analysis of the XXI
climate extremes. In contrast to previous research, our study places a strong
emphasis on enhancing the estimation of the tails of future climate parameter
distributions. The latter supports decision-makers, enabling them to better
assess climate-related risks across the globe.
