Jaume Santero, FernandoBarriopedro Cepero, DavidGarcía Herrera, RicardoCalvo, NataliaSalcedo Sanz, Sancho2023-06-162023-06-162020-05-132045-232210.1038/s41598-020-64459-6https://hdl.handle.net/20.500.14352/6500© The Authors. This work was supported by the Ministerio de Economía y Competitividad del Gobierno de España through the PALEOSTRAT (CGL2015-69699-R) project. Jaume-Santero was funded by grant BES-2016-077030 from the Ministerio de Ciencia, Innovación y Universidades del Gobierno de España and the European Social Fund.In the Era of exponential data generation, increasing the number of paleoclimate records to improve climate feld reconstructions might not always be the best strategy. By using pseudo-proxies from diferent model ensembles, we show how biologically-inspired artifcial intelligence can be coupled with diferent reconstruction methods to minimize the spatial bias induced by the non-homogeneous distribution of available proxies. The results indicate that small subsets of records situated over representative locations can outperform the reconstruction skill of the full proxy network, even in more realistic pseudo-proxy experiments and observational datasets. These locations highlight the importance of high-latitude regions and major teleconnection areas to reconstruct annual global temperature felds and their responses to external forcings and internal variability. However, low frequency temperature variations such as the transition between the Medieval Climate Anomaly and the Little Ice Age are better resolved by records situated at lower latitudes. According to our idealized experiments a careful selection of proxy locations should be performed depending on the targeted time scale of the reconstructed feldengAtribución 3.0 Españajournal articlehttp://dx.doi.org/10.1038/s41598-020-64459-6https://www.nature.com/open access52ClimateComputationalEnsembleFísica atmosférica2501 Ciencias de la Atmósfera