Studies on recent climate trends from the Himalayan range are limited, and even completely absent at high elevation (> 5000 m a.s.l.). This study specifically explores the southern slopes of Mt. Everest, analyzing the time series of temperature and precipitation reconstructed from seven stations located between 2660 and 5600 m a.s.l. during 1994–2013, complemented with the data from all existing ground weather stations located on both sides of the mountain range (Koshi Basin) over the same period. Overall we find that the main and most significant increase in temperature is concentrated outside of the monsoon period. Above 5000 m a.s.l. the increasing trend in the time series of minimum temperature (+0.072 °C yr−1) is much stronger than of maximum temperature (+0.009 °C yr−1), while the mean temperature increased by +0.044 °C yr−1. Moreover, we note a substantial liquid precipitation weakening (−9.3 mm yr−1) during the monsoon season. The annual rate of decrease in precipitation at higher elevations is similar to the one at lower elevations on the southern side of the Koshi Basin, but the drier conditions of this remote environment make the fractional loss much more consistent (−47% during the monsoon period). Our results challenge the assumptions on whether temperature or precipitation is the main driver of recent glacier mass changes in the region. The main implications are the following: (1) the negative mass balances of glaciers observed in this region can be more ascribed to a decrease in accumulation (snowfall) than to an increase in surface melting; (2) the melting has only been favoured during winter and spring months and close to the glaciers terminus; (3) a decrease in the probability of snowfall (−10%) has made a significant impact only at glacier ablation zone, but the magnitude of this decrease is distinctly lower than the observed decrease in precipitation; (4) the decrease in accumulation could have caused the observed decrease in glacier flow velocity and the current stagnation of glacier termini, which in turn could have produced more melting under the debris glacier cover, leading to the formation of numerous supraglacial and proglacial lakes that have characterized the region in the last decades.

Understanding the response of Himalayan glaciers to global warming is vital because of their role as a water source for the Asian subcontinent. However, great uncertainties still exist on the climate drivers of past and present glacier changes across scales. Here, we analyse continuous hourly climate station data from a glacierized elevation (Pyramid station, Mount Everest) since 1994 together with other ground observations and climate reanalysis. We show that a decrease in maximum air temperature and precipitation occurred during the last three decades at Pyramid in response to global warming. Reanalysis data suggest a broader occurrence of this effect in the glacierized areas of the Himalaya. We hypothesize that the counterintuitive cooling is caused by enhanced sensible heat exchange and the associated increase in glacier katabatic wind, which draws cool air downward from higher elevations. The stronger katabatic winds have also lowered the elevation of local wind convergence, thereby diminishing precipitation in glacial areas and negatively affecting glacier mass balance. This local cooling may have partially preserved glaciers from melting and could help protect the periglacial environment.

Snow represents a fundamental water resource for mountain and lowland areas. Changes in the frequency and magnitude of snow droughts can significantly impact societies and ecosystems that rely on snowmelt to satisfy their water demands. Here we documented and quantified the snow drought that affected the Italian Alps during the early 2020s. We used 15 long-term snow-depth series (period 1930–2023, elevation range: 864–2200 m a.s.l.) to simulate the snow water equivalent (SWE), in conjunction with climatic reanalysis data and river discharge observations. We found that the March SWE anomaly in 2022 reached the lowest value in the last century, due to an unprecedented combination of drier- and warmer-than-normal conditions in the period December 2021–March 2022. This event contributed to causing critical hydrological conditions in the Po and Adige rivers which, during summer 2022, experienced the worst hydrological drought ever recorded. Despite its unprecedented magnitude, the snow drought in 2022 is part of a recent pattern of increased intensity and frequency of snow-drought events since the 1990s, due to the combined increasing occurrence of warmer- and drier-than-normal climatic conditions during the snow season. Remarkably, three out of the five most severe snow-drought events occurred in the last five years, with exceptional snow-drought conditions even occurring in the last two consecutive winters, 2022 and 2023. The snow-drought conditions that occurred in the early 2020s in the Italian Alps warn of the pressing need for the implementation of impact mitigation measures to adapt to the fast changing snow and climatic conditions.

Various downscaling techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Such techniques may be grouped into two downscaling approaches: the deterministic dynamical downscaling (DD) and the statistical downscaling (SD). Although SD has been traditionally seen as an alternative to DD, recent works on statistical downscaling have aimed to combine the benefits of these two approaches. The overall objective of this study is to assess whether a DD processing performed before the SD permits to obtain more suitable climate scenarios for basin scale hydrological applications starting from GCM simulations. The case study presented here focuses on the Apulia region (South East of Italy, surface area about 20 000 km²), characterised by a typical Mediterranean climate; the monthly cumulated precipitation and monthly mean of daily minimum and maximum temperature distribution were examined for the period 1953–2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile correction. The SD resulted efficient in reducing the mean bias in the spatial distribution at both annual and seasonal scales, but it was not able to correct the miss-modelled non-stationary components of the GCM dynamics. The DD provided a partial correction by enhancing the spatial heterogeneity of trends and the long-term time evolution predicted by the GCM. The best results were obtained through the combination of both DD and SD approaches.