Carro Calvo, LeopoldoJaume Santero, FernandoGarcía Herrera, Ricardo FranciscoSalcedo Sanz, Sancho2023-06-172023-06-172021-010177-798X10.1007/s00704-020-03396-whttps://hdl.handle.net/20.500.14352/8088© The Author(s) 2020. This work was supported by the Ministerio de Economía y Competitividad through the PALEOSTRAT (CGL2015-69699-R) and TIN2017-85887-C2-2-P projects. Jaume-Santero was funded by grant BES-2016-077030 from the Ministerio de Economía y Competitividad and the European Social Fund.In this paper, we show a new clustering technique (k-gaps) aiming to generate a robust regionalization using sparse climate datasets with incomplete information in space and time. Hence, this method provides a new approach to cluster time series of different temporal lengths, using most of the information contained in heterogeneous sets of climate records that, otherwise, would be eliminated during data homogenization procedures. The robustness of the method has been validated with different synthetic datasets, demonstrating that k-gaps performs well with sample-starved datasets and missing climate information for at least 55% of the study period. We show that the algorithm is able to generate a climatically consistent regionalization based on temperature observations similar to those obtained with complete time series, outperforming other clustering methodologies developed to work with fragmentary information. k-Gaps clusters can therefore provide a useful framework for the study of long-term climate trends and the detection of past extreme events at regional scales.engAtribución 3.0 Españahttps://creativecommons.org/licenses/by/3.0/es/journal articlehttp://dx.doi.org/10.1007/s00704-020-03396-whttps://link.springer.com/open access52Clustering techniquesClimatological time seriesClimate trendsRegional analysisFísica atmosférica2501 Ciencias de la Atmósfera