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Abstract Within High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier‐scale variations in precipitation. This study aimed to determine the current characteristics of orographic precipitation gradients (OPG) by curve‐fitting daily precipitation as a function of elevation from a 15‐year, 4‐km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu‐Kush mountain ranges. To facilitate precipitation curve‐fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion‐corrected values and anF‐testp‐value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using ‐means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra‐seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically‐forced heavy‐precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically‐based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA. 

Southern Andean glaciers contribute substantially to global sea-level rise. Unfortunately, mass balance estimates prior to 2000 are limited, hindering our understanding of the evolution of glacier mass changes over time. Elevation changes over 1976/1979 to 2000 derived from historical KH-9 Hexagon imagery and NASADEM provide the basis for geodetic mass balance estimates for subsets of the Northern Patagonian Icefield (NPI) and the Southern Patagonian Icefield (SPI), extending current mass balance observations by ∼20 years. Geodetic mass balances were −0.63 ± 0.03 m w.e. yr −1 for 63% of the NPI and −0.33 ± 0.05 m w.e. yr −1 for 52% of the SPI glacierized areas for this historical period. We also extend previous estimates temporally by 25% using NASADEM and ASTER elevation trends for the period 2000 to 2020, and find geodetic mass balances of −0.86 ± 0.03 m w.e. yr −1 for 100% of the NPI and −1.23 ± 0.04 m w.e. yr −1 for 97% of the SPI glacierized areas. 2000–2020 aggregations for the same areas represented in the 1976/1979 to 2000 estimates are −0.78 ± 0.03 m w.e. yr −1 in the NPI and −0.80 ± 0.04 m w.e. yr −1 on the SPI. The significant difference in SPI geodetic mass balance in the modern period for 100% vs. 52% of the glacierized area suggests subsampling leads to significant biases in regional mass balance estimates. When we compare the same areas in each time period, the results highlight an acceleration of ice loss by a factor of 1.2 on the NPI and 2.4 on the SPI in the 21st century as compared to the 1976/1979 to 2000 period. While lake-terminating glaciers show the most significant increase in mass loss rate from 1976/1979–2000 to 2000–2020, mass balance trends are highly variable within glaciers of all terminus environments, which suggests that individual glacier sensitivity to climate change is dependent on a multitude of morphological and climatological factors.