Seasonal snow and glaciers in arid mountain regions are essential in sustaining human populations, economic activity, and ecosystems, especially in their role as reservoirs. However, they are threatened by global atmospheric changes, in particular by variations in air temperature and their effects on precipitation phase, snow dynamics and mass balance. In arid environments, small variations in snow mass and energy balance can produce large changes in the amount of available water. This paper provides insights into the impact of global warming on the mass balance of the seasonal snowpack in the mountainous Copiapó river basin in northern Chile. A dataset from an experimental station was combined with reanalysis data to run a physically based snow model at site and catchment scales. The basin received an average annual precipitation of approximately 130 mm from 2001 to 2016, with sublimation losses higher than 70% of the snowpack. Blowing snow sublimation presented an orographic gradient resultant from the decreasing air temperature and windy environment in higher elevations. Under warmer climates, the snowpack will remain insensitive in high elevations (>4000 m a.s.l.), but liquid precipitation will increase at lower heights.

The climate in Continental Chile is marked by strong latitudinal and elevation gradients, exacerbated by diverse geographical features, such as the Andes. Despite previous studies projecting warmer and dryer conditions for most of the territory, there is concern about the robustness (i.e. level of agreement among models) of changes projected for its magnitude, not only for the impact on climate indices across this domain but also to identify changes in the spatial distribution of climate classes. Hence, we statistically downscaled and bias-corrected daily CMIP6 model outputs for continental Chile, using a multivariate bias correction method, to project climate changes under the SSP5-8.5 scenario. The results reveal that General Circulation Models (GCMs) project increased dryness across the study domain by the end of the 21^st century, especially in Central Chile (−30% in precipitation), with notable sensitivities of precipitation projections to the implementation of bias correction methods in the northern and austral macrozones. Temperature projections show less dispersion, with higher increments in northern Chile and the Andes (4 ^∘C–5 ^∘C). Notable shifts in the extension of Köppen–Geiger climate classes are projected for the next decades, with the expansion of deserts in northern Chile and the prevalence of temperate climates with dry summers in central Chile. The Andes subdomain is expected to face the most dramatic changes in Köppen–Geiger classes (inter-model agreement > 70%). Surprisingly, despite the large spread in GCM projections, there is high agreement among models regarding spatial changes in climate classes. Additionally, our results project drastic reductions in snowfall across the Andes, with higher freezing level heights that may exacerbate flooding and landslide risk across the country.

Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias‐corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub‐periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade‐offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques.