Large-sample hydrological datasets are essential for advancing hydrological understanding and modelling across diverse environments, yet they remain scarce in South America, particularly in tropical Andean regions with strong climatic and physiographic gradients. Here, we present CAMELS-PE v1.0.1, a large-sample hydrological dataset for Peru that provides daily hydrometeorological time series and catchment attributes for 136 catchments. The dataset includes observed and simulated streamflow, meteorological forcing variables, geospatial layers, and attributes describing topography, climate, hydrological behaviour, land cover, geology, soils, and human intervention. All variables were generated under a consistent workflow involving temporal harmonisation, catchment-scale aggregation, and standardised formatting, with dedicated screening applied to observed streamflow records. The resulting dataset was evaluated through consistency checks across metadata and catchment attributes, together with plausibility analyses of regional hydroclimatic patterns. By capturing Peru’s pronounced environmental contrasts, CAMELS-PE expands the representation of tropical Andean and Amazonian headwater catchments within the CAMELS framework and provides an open benchmark dataset for hydrological modelling, regionalisation, climate–streamflow analysis, prediction in ungauged basins, and machine-learning applications. CAMELS-PE is publicly available through Zenodo at <a href="https://doi.org/10.5281/zenodo.21195425" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.21195425</a> (Llauca et al., 2026) and is supported by the RCamelsPE R package.