Quasi-global, land-only, high-resolution and spatially averaged climate variables from downscaled CMIP6 models for climate impact research

Abstract

Global climate models (GCMs) are essential tools for understanding the climate system and projecting its evolution under different scenarios. However, differences in model construction introduce uncertainties, and GCMs have coarse resolution and inherent biases, limiting their effectiveness for informing local or regional adaptation and mitigation planning. We apply the statistical Double Bias-Corrected Constructed Analogues (DBCCA) method to generate bias-corrected and downscaled climate model outputs at daily and 0.1° spatial resolution for 1985–2100, with a specific focus on supporting tropical health-related impact research. Our quasi-global, high-resolution outputs are based on six GCMs from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) under two emission scenarios (SSP2-4.5 and SSP5-8.5), covering 12 land-only domains between 60 °N and 60 °S. Moreover, many impact researchers rely on accessible data aggregated to administrative units, often weighted by population, rather than gridded data, as these align more directly with policy- and decision-making. Based on our bias-corrected and downscaled model outputs, we also provide user-ready (population-weighted) spatially aggregated climate variables at administrative unit levels (0-2) for 110 countries prioritized for tropical disease research.