Global and regional long-term climate forecasts: a heterogeneous future

Climate is a long-term issue, and as such, climate forecasts should be designed with a long-term perspective. These forecasts are critical for crafting mitigation policies aimed at achieving one of the primary objectives of the Paris Climate Agreement (PCA) and for designing adaptation strategies to alleviate the adverse effects of climate change. Furthermore, they serve as indispensable tools for assessing climate risks and guiding the green transition effectively. This paper introduces a straightforward method for generating long-term temperature density forecasts using observational data, leveraging the realized quantile methodology developed by Gadea and Gonzalo (JoE, 2020). This methodology transforms unconditional quantiles into time series objects. The resulting forecasts complement those produced by physical climate models, which primarily focus on average temperature values. By contrast, our density forecasts capture broader distributional characteristics, including spatial disparities that are often obscured in mean-based projections. The proposed approach involves conducting an outof-sample forecast model competition and integrating the forecasts from the resulting Pareto-superior models. This method reduces dependency on any single forecast model, enhancing the robustness of the results. Additionally, recognizing climate change as a non-uniform phenomenon, our approach emphasizes the importance of analyzing climate data from a regional perspective, providing differentiated predictions to address the complexities of a heterogeneous future. This regional focus underscores the necessity of accounting for spatial disparities to better assess risks and develop effective policies for mitigation, adaptation, and compensation. Finally, this paper advocates that future climate agreements and policymakers should prioritize analyzing the entire temperature distribution rather than focusing solely on average values.