ArticleScientific data2023
High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Observed and projected climate extremes in northwest highlands of Ethiopia and their implications in potato-based farming systems.Scientific reports · 2026Article
- Global monthly CMIP6-downscaled high-resolution (1 km) near-surface air temperature dataset (1950-2100).Scientific data · 2025Article
- Ensemble downscaled climate dataset for Alaska and Hawaii under historical and future conditions.Scientific data · 2025Article
- Meteorological drought under historical and future climate scenarios in North Gojjam sub-basin, Abay River basin of Ethiopia.PloS one · 2025Article
- Projected hydroclimatic changes in Xinjiang under bias-corrected CMIP6 scenarios.Frontiers in plant science · 2025Article
- High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method.Scientific data · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation models (GCMs) are the main sources of critical information. However, these simulations are not only coarse in resolution but also associated with biases and high uncertainty. To make the simulations useful for impact modeling at regional and local level, we utilized the bias correction constructed analogues with quantile mapping reordering (BCCAQ) statistical downscaling technique to produce a 10 km spatial resolution climate change projections database based on 16 CMIP6 GCMs under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The downscaling strategy was evaluated using a perfect sibling approach and detailed results are presented by taking two contrasting (the worst and best performing models) GCMs as a showcase. The evaluation results demonstrate that the downscaling approach substantially reduced model biases and generated higher resolution daily data compared to the original GCM outputs.
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Registered trials
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