High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset

Adrian Huerta, Cesar Aybar, Noemi Imfeld, Kris Correa, Oscar Felipe-Obando, Pedro Rau, Fabian Drenkhan, Waldo Lavado-Casimiro

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Gridded high-resolution climate datasets are increasingly important for a wide range of modelling applications. Here we present PISCOt (v1.2), a novel high spatial resolution (0.01°) dataset of daily air temperature for entire Peru (1981–2020). The dataset development involves four main steps: (i) quality control; (ii) gap-filling; (iii) homogenisation of weather stations, and (iv) spatial interpolation using additional data, a revised calculation sequence and an enhanced version control. This improved methodological framework enables capturing complex spatial variability of maximum and minimum air temperature at a more accurate scale compared to other existing datasets (e.g. PISCOt v1.1, ERA5-Land, TerraClimate, CHIRTS). PISCOt performs well with mean absolute errors of 1.4 °C and 1.2 °C for maximum and minimum air temperature, respectively. For the first time, PISCOt v1.2 adequately captures complex climatology at high spatiotemporal resolution and therefore provides a substantial improvement for numerous applications at local-regional level. This is particularly useful in view of data scarcity and urgently needed model-based decision making for climate change, water balance and ecosystem assessment studies in Peru.

Original languageEnglish
Article number847
JournalScientific Data
Issue number1
StatePublished - Dec 2023


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