The dataset is the output of a statistical model which downscales ERA5 monthly precipitation data using gauge measurements from the Upper Beas and Sutlej Basins in the Western Himalayas. Multi-Fidelity Gaussian Processes (MFGPs) are used to generate more accurate precipitation values between 1980 and 2012, including over ungauged areas of the basins. MFGPs are a probabilistic machine learning method that provides principled uncertainty estimates via the prediction of probability distributions. These predictions can therefore be used to estimate the likelihood of extreme precipitation events which have led to droughts, floods, and landslides. Funding from UK Engineering and Physical Sciences Research Council [grant number: 2270379].

Precipitation and near-surface temperature data from the Coupled Model Intercomparison Project phase 5 (CMIP5 models) are statistically downscaled to create these gridded datasets over the Rio Santa River Basin (in the Cordillera Blanca; d02) and the Vilcanota-Urubamba region (d03) at 4 km horizontal resolution, from 2019-2100. The bias-corrected WRF data found in the related dataset are used as the observational truth for the historical period 1980-2018, and the data are downscaled using an empirical quantile mapping technique. Two representative concentration pathways (RCP) have been downscaled, RCP 4.5 and RCP 8.5, from 30 CMIP5 models. The daily total precipitation and daily minimum and maximum temperature at 2 m are downscaled, and the daily average and monthly average temperatures are calculated using the hourly temperature (not archived due to space constraints). The potential evapotranspiration is estimated from the downscaled precipitation and temperature data, using the Hargreaves equation. These data were corrected as part of the PEGASUS (Producing EnerGy and preventing hAzards from SUrface water Storage in Peru) and Peru GROWS (Peruvian Glacier Retreat and its Impact on Water Security) projects. The datasets were created to assess future climate in the Peruvian Andes, as a basis to determine future climate in the region, and as an input for glaciological and hydrological models. The data were created on the JASMIN supercomputer. The creation of this data was conducted under the Peru GROWS and PEGASUS projects, which were both funded by NERC (grants NE/S013296/1 and NE/S013318/1, respectively) and CONCYTEC through the Newton-Paulet Fund. The Peruvian part of the Peru GROWS project was conducted within the framework of the call E031-2018-01-NERC "Glacier Research Circles", through its executing unit FONDECYT (Contract No. 08-2019-FONDECYT).

Based on the bias-corrected WRF data and the statistically downscaled CMIP5 data (see related datasets), six climate change detection indices are calculated, based on the Expert Team on Climate Change Detection and Indices (ETCCDI). Each index is calculated for the control period (1980-2018) from the bias-corrected WRF data, and the future (2019-2100) for each of the 30 CMIP5 models. Six of the ETCCDI climate indices are calculated here (taken from Zhang (2011)): the simple precipitation intensity index describing the total annual precipitation on wet days; the annual total precipitation falling on days where precipitation is above the 95th percentile of the 1980-2018 period; the number of dry days (precipitation under 1 mm) in a year (a variation on "continuous dry days" given in Zhang (2011); the annual average monthly maximum temperature; the warm spell duration index describing the annual count of days with at least 6 consecutive days above the 90th percentile of daily maximum temperature from 1980-2018; the number of frost days (minimum daily temperature below 0 deg C). These data were corrected as part of the PEGASUS (Producing EnerGy and preventing hAzards from SUrface water Storage in Peru) and Peru GROWS (Peruvian Glacier Retreat and its Impact on Water Security) projects. The datasets were created to assess future climate in the Peruvian Andes. The data were created on the JASMIN supercomputer. The creation of this data was conducted under the Peru GROWS and PEGASUS projects, which were both funded by NERC (grants NE/S013296/1 and NE/S013318/1, respectively) and CONCYTEC through the Newton-Paulet Fund. The Peruvian part of the Peru GROWS project was conducted within the framework of the call E031-2018-01-NERC "Glacier Research Circles", through its executing unit FONDECYT (Contract No. 08-2019-FONDECYT).