Two netcdf files are provided that contain daily precipitation amounts for January 1979 - July 2017 from the RACMO version 3p2 limited area, atmosphere-only model. The model is described in van Wessem, J. M., C. H. Reijmer, M. Morlighem, J. Mouginot, E. Rignot, B. Medley, and E. van Meijgaard, (2014) Improved representation of East Antarctic surface mass balance in a regional atmospheric climate model, Journal of Glaciology, 60, 761-770. The model was run over a 262 by 240 grid point domain covering Antarctica and parts of the Southern Ocean. The model was forced at the lateral boundaries by data from the European Centre for Medium-range Weather Forecasting (ECMWF) Interim reanalysis (ERA-Interim). Flags are provided for extreme precipitation events. A precipitation day was taken as a daily total of precipitation of greater than 0.02 mm. Extreme precipitation events were then taken as days when daily precipitation amount was greater than the 90th percentile of the daily precipitation values over the period 1979 - 2016.

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].

High-resolution simulations of daily precipitation over the Beas and Sutlej basins in the Himalaya from 1980 to 2012 were conducted using the Weather Research and Forecasting (WRF) model by the British Antarctic Survey, Cambridge, UK. It was shown that applying a non-linear bias-correction method to the model precipitation output resulted in much better results. The work formed part of the project ''Sustaining Himalayan Water Resources in a Changing Climate (SusHi-Wat)'' during 2015 to 2018, and was funded by the UK Natural Environmental Research Council grant number NE/N015592/1. The datasets produced are necessary as accurate fine-scale estimates of precipitation over catchments in the Himalaya mountain range are required for providing input to hydrological models, as well as identifying precipitation extremes for assessing hydro-meteorological hazards.

High-resolution simulation of summer climate over West Antarctica using the Polar-optimised version of the Weather Research and Forecasting (WRF) model conducted at British Antarctic Survey, Cambridge, UK. Runs are conducted for summer (January-centred) 1980-2015, i.e. from December 1979 to February 2015, for December, January and February (DJF). Experiments were carried out for the NERC West Antarctic Grant (NE/K00445X/1) during 2014-2017. The project is aimed at understanding the variability and climatology over the West Antarctic ice sheet and ice shelves as well as to project the future change over the twenty-first century. The model outer domain encompasses the West Antarctic ice sheet and a large part of the surrounding ocean at 45 km horizontal grid spacing, and the nested (one-way) inner domain covers the Amundsen Sea Embayment at 15 km grid spacing. The model uses vertical eta coordinates with both domains have a model top of 50 hPa, and 30 vertical levels.

Temperature and precipitation data from the Weather Research and Forecasting model are bias-corrected against observations to create these bias-corrected gridded datasets over the Rio Santa River Basin (in the Cordillera Blanca) at 4 km horizontal resolution (d02), the Vilcanota-Urubamba region at 4 km horizontal resolution (d03) and the upper region of the Rio Santa River Basin at 800 m horizontal resolution (d04). The raw WRF data can be found in the related dataset. Full details of the bias-correction can be found in Fyffe et al., (2021). 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 past 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 using the British Antarctic Survey high performance computer. 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).

Three micro-power Automatic Weather Stations (AWS) with two sonic ranging sensors were deployed at field-sites situated at Rothschild Island, Latady Island and Smyley Island in January 2005. The AWS instruments included a wind vane and two humicaps on the mast and two sonic ranging sensors mounted on separate horizontal scaffold poles. The AWS data collected contributed to a project concerned with understanding how air mass origin and meteorology affect the mass accumulation of snow in areas of the Antarctic Peninsula, and how the atmosphere''s properties are preserved in the snow.

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).