The data was produced employing the Advanced Research Weather Research and Forecasting model (WRF) version 4.1.2 (Skamarock et al., 2019) for the dynamical downscaling of GCM data. WRF is a fully compressible non-hydrostatic atmospheric simulation system. Two sensitivity simulations were conducted using 15-year time slices for the present day and the mid-Pliocene simulated by ECHAM5 as initial and boundary conditions (Mutz et al., 2018; Botsyun et al., 2020). Except for the atmospheric forcing data, other parameters were the same in both simulations. The model domain has a grid spacing of 30 km. In the vertical direction, 28 terrain-following eta-levels were used. The model time steps are 120 seconds with a 6 hourly data output and are aggregated to daily values in post processing. The boundary conditions were updated every 6 h. The daily re-initialization strategy from Maussion et al. (2011) and Maussion et al. (2014) were employed: each simulation starts at 12 UTC and contains 36 h, with the first 12 h as the spin-up time. This strategy kept the large-scale circulation patterns simulated by WRF closely constrained by the forcing data, while concurrently allowing WRF to develop the mesoscale atmospheric features. Physical parameterization schemes were consistent with the ones used for high-resolution dynamical downscaling in High Mountain Asia in Wang et al. (2021). The data format follows the guidelines of the [UC]² Data Standard (http://www.uc2-program.org/uc2_data_standard.pdf).

The data was produced employing the Advanced Research Weather Research and Forecasting model (WRF) version 4.1.2 (Skamarock et al., 2019) for the dynamical downscaling of GCM data. WRF is a fully compressible non-hydrostatic atmospheric simulation system. Two sensitivity simulations were conducted using 15-year time slices for the present day and the mid-Pliocene simulated by ECHAM5 as initial and boundary conditions (Mutz et al., 2018; Botsyun et al., 2020). Except for the atmospheric forcing data, other parameters were the same in both simulations. The model domain has a grid spacing of 30 km. In the vertical direction, 28 terrain-following eta-levels were used. The model time steps are 120 seconds with a 6 hourly data output and are aggregated to daily values in post processing. The boundary conditions were updated every 6 h. The daily re-initialization strategy from Maussion et al. (2011) and Maussion et al. (2014) were employed: each simulation starts at 12 UTC and contains 36 h, with the first 12 h as the spin-up time. This strategy kept the large-scale circulation patterns simulated by WRF closely constrained by the forcing data, while concurrently allowing WRF to develop the mesoscale atmospheric features. Physical parameterization schemes were consistent with the ones used for high-resolution dynamical downscaling in High Mountain Asia in Wang et al. (2021). The data format follows the guidelines of the [UC]² Data Standard (http://www.uc2-program.org/uc2_data_standard.pdf).

The climatological dataset was produced using the Weather and Research Forecasting (WRF) model, version 4.2.2, configured with two nested domains at 10 km (D1) and 2 km (D2) horizontal grid spacing. It covers most of the South Island of New Zealand and is centered over Brewster Glacier in the Southern Alps. The model was forced every three hours by ERA5 reanalysis data at its outer lateral boundaries. The dataset spans the period of 1 January 2005 to 31 December 2020, providing daily output in the outer domain (D1) and 3-hourly output in the innermost domain (D2). The data provided here are a selection of daily averages from the inner WRF domain (D2; 2-km grid spacing). They are distributed among three different file types containing 4-dimensional, 3-dimensional and time-invariant output variables, respectively. For the 4-dimensional fields, perturbation and base-state atmospheric pressure (WRF variables P and PB) and geopotential (PH and PHB) were combined to produce full model fields (PRES and GEOPT). Perturbation potential temperature (T) was converted to total potential temperature (THETA). Wind vectors (U,V, and W) were converted to mass points and rotated to earth coordinates. ------- Acknowledgements: The modeling and related research was supported by the German Research Foundation (DFG) grant no. 453305163. The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project b128dc / ATMOS ("Numerical atmospheric modeling for the attribution of climate change and for model improvement"). NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the German Research Foundation (DFG) – 440719683.