This study examines the impact of climate risk-related variables on mortality patterns across diverse population groups. An innovative version of the Lee-Carter model is proposed, incorporating a climate risk-related variable - specifically, \(\text {CO2}\) emissions - to enhance the accuracy of mortality prediction. A three-step estimation process using Ordinary Least Squares is employed to calibrate the model parameters. Experiments conducted on data from 42 populations indicate that integrating climate risk information leads to more accurate mortality predictions. Especially, combining climate variables with macroeconomic indicators such as GDP yields further improvements in forecast performance.