ArticleScientific data2020
High-resolution monthly precipitation and temperature time series from 2006 to 2100.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Global monthly CMIP6-downscaled high-resolution (1 km) near-surface air temperature dataset (1950-2100).Scientific data · 2025Article
- Dispersal Ability Reduces Thermal Specialization and Prevents Climate-Driven Extinctions in a Neotropical Rainforest.Global change biology · 2025Article
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- Climate and urbanization drive changes in the habitat suitability of Schistosoma mansoni competent snails in Brazil.Nature communications · 2024Article
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- Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014-2021.Parasites & vectors · 2024Article
- Pervasive associations between dark septate endophytic fungi with tree root and soil microbiomes across Europe.Nature communications · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting future climatic conditions at high spatial resolution is essential for many applications and impact studies in science. Here, we present monthly time series data on precipitation, minimum- and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation at ~5 km spatial resolution globally for the years 2006-2100. We validated the performance of the downscaling algorithm by comparing model output with the observed climate of the historical period 1950-1969.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.