In the summer of 2022, a record-breaking heatwave and drought event occurred in the Yangtze River (YR) Basin of China, causing great damage to the society and ecosystem. However, the role of land-atmosphere (LA) interactions in driving and reinforcing this event has not been fully studied. In this study, using air temperature, soil moisture (SM), surface sensible heat fluxes, surface latent heat fluxes and radiation fluxes data from ERA5, we analyze the process of this event and reveal the contribution of the LA feedbacks. The results indicate that during the 2022 YR Basin heatwave and drought event, the regional average maximum air temperature and SM reached unprecedented levels of 2.7 standard deviations (SDs) and -3.5 SDs, respectively, compared to the climatology from 1980 to 2021. In August 2022, SM rapidly declined, pushing the region into a rare dry state. The dry soil increased the sensitivity of daily maximum air temperature to SM, intensifying the occurrence of heatwaves in the area. Simultaneously, increased downward solar radiation reached surface and most of that converted to sensible heat fluxes due to low soil moisture limitations leading to elevated air temperatures. While similar events have been reported multiple times in regions like Europe and western North America, their occurrence in the moist region of the YR Basin of China is exceptionally rare, which suggests an increasing likelihood of such extreme events in this region. Land-atmosphere interactions play an increasingly crucial role in exacerbating extreme conditions, and therefore, more studies such as this are needed for improving predictability of extreme events on a sub-seasonal time scale.

Vegetation growth is adversely impacted by multiple climate extremes related to the water and thermal stress over the Tibetan Plateau (TP). However, it remains unknown at which stress level these climate extremes can trigger the abrupt shifts of vegetation response to climate extremes and result in the maximum vegetation response across TP. To fill this knowledge gap, we combined the hydrometeorological data and the satellite-derived vegetation index to detect two critical thresholds that determine the response of vegetation productivity to droughts, high-temperature extremes, and low-temperature extremes, respectively, during 2001-2018. Our results show that the response of vegetation productivity to droughts rapidly increases once crossing -1.41 +/- 0.6 standard deviation (sigma) below the normal conditions of soil moisture. When crossing -2.98 sigma +/- 0.9 sigma, vegetation productivity is maximum damaged by droughts. High-temperature extremes, which have the two thresholds of 1.34 sigma +/- 0.4 sigma and 2.31 sigma +/- 0.4 sigma over TP, are suggested to trigger the strong response of vegetation productivity at a milder stress level than low-temperature extremes (two thresholds: -1.44 sigma +/- 0.5 sigma and -2.53 sigma +/- 0.8 sigma). Moreover, we found the compounded effects of soil moisture deficit in reducing the threshold values of both high- and low-temperature extremes. Based on the derived thresholds of climate extremes that impact vegetation productivity, Earth System Models project that southwestern TP and part of the northeastern TP will become the hotspots with a high exposure risk to climate extremes by 2100. This study deciphers the high-impact extreme climates using two important thresholds across TP, which advances the understanding of the vegetation response to different climate extremes and provides a paradigm for assessing the impacts of climate extremes on regional ecosystems.

Hydroclimatic stresses can negatively impact crop production via water deficits (low soil water supply and high atmospheric demand) or surpluses (high soil water supply and low atmospheric demand). However, the impact of both stresses on crop yields at regional scales is not well understood. Here we quantified yield sensitivities and corresponding spatio-temporal yield losses of US rainfed maize, soybeans, sorghum, and spring wheat to hydroclimatic stresses by considering the joint impacts of root-zone soil moisture and atmospheric evaporative demand from 1981 to 2020. We show that crop yields can be reduced similarly by two major hydroclimatic hazards, which are defined as the most yield damaging conditions over time: 'Low Supply + High Demand' and 'High Supply + Low Demand'. However, more exposure to 'Low Supply + High Demand' hazard led to the largest annual yield losses (7%-17%) across all four crops over time. Modeled yield losses due to these hazards were significantly associated with crop insurance lost costs. The extent of yield losses varies considerably by crop and location, highlighting the need for crop-specific and regionally tailored adaptation strategies.

As an icon of anthropogenic climate change, alpine glaciers are highly sensitive to climate change. However, there remain research gaps regarding trends in climate extremes in glacierized regions and their relationship with local glacier mass balance. In this study, these re-lationships and their underlying links were explored in a typical glacierized region in the Eastern Tianshan Mountains, China, from 1959 to 2018. All warm extremes exhibited increasing trends that intensified dramatically from the 1990s. Meanwhile, decreasing trends were found for all cold extremes except for the temperatures of the coldest days and coldest nights. All of the precipitation extremes demonstrated increasing trends, except for consecutive dry days and consecutive wet days. Statistically significant positive/negative correlations were detected between glacier mass balance and six warm extremes (TN90p, TX90p, SU99p, TR95p, TXx, and TNx)/four cold extremes (TN10p, TX10p, FD0, and ID0). Simulation results showed that the impact of the intensity/frequency of the warm extremes (TN90p, TX90p, SU99p, and TR95p) on glacier ablation was remarkable and the effect of the cold extremes (FD0 and ID0) on accumulation was also significant. Additionally, the increases in the intensity and frequency of most climate extremes seemed more remarkable in glacierized regions than in non-glacierized regions. Hence, studies on glacier-climate interactions should focus greater attention on the impacts of climate extremes on glacier evolution.

On the Arctic Coastal Plain (ACP) in northern Alaska (USA), permafrost and abundant surface-water storage define watershed hydrological processes. In the last decades, the ACP landscape experienced extreme climate events and increased lake water withdrawal (LWW) for infrastructure construction, primarily ice roads and industrial operations. However, their potential (combined) effects on streamflow are relatively underexplored. Here, we applied the process-based, spatially distributed hydrological and thermal Water Balance Simulation Model (10 m spatial resolution) to the 30 km(2) Crea Creek watershed located on the ACP. The impacts of documented seasonal climate extremes and LWW were evaluated on seasonal runoff (May-August), including minimum 7-day mean flow (MQ7), the recovery time of MQ7 to pre-perturbation conditions, and the duration of streamflow conditions that prevents fish passage. Low-rainfall scenarios (21% of normal, one to three summers in a row) caused a larger reduction in MQ7 (-56% to -69%) than LWW alone (-44% to -58%). Decadal-long consecutive LWW under average climate conditions resulted in a new equilibrium in low flow and seasonal runoff after 3 years that included a disconnected stream network, a reduced watershed contributing area (54% of total watershed area), and limited fish passage of 20 days (vs. 6 days under control conditions) throughout summer. Our results highlight that, even under current average climatic conditions, LWW is not offset by same-year snowmelt as currently assumed in land management regulations. Effective land management would therefore benefit from considering the combined impact of climate change and industrial LWWs.

Uncertainties in the climate response to a doubling of atmospheric CO2 concentrations are quantified in a perturbed land surface parameter experiment. The ensemble of 108 members is constructed by systematically perturbing five poorly constrained land surface parameters of global climate model individually and in all possible combinations. The land surface parameters induce small uncertainties at global scale, substantial uncertainties at regional and seasonal scale and very large uncertainties in the tails of the distribution, the climate extremes. Climate sensitivity varies across the ensemble mainly due to the perturbation of the snow albedo parameterization, which controls the snow albedo feedback strength. The uncertainty range in the global response is small relative to perturbed physics experiments focusing on atmospheric parameters. However, land surface parameters are revealed to control the response not only of the mean but also of the variability of temperature. Major uncertainties are identified in the response of climate extremes to a doubling of CO2. During winter the response both of temperature mean and daily variability relates to fractional snow cover. Cold extremes over high latitudes warm disproportionately in ensemble members with strong snow albedo feedback and large snow cover reduction. Reduced snow cover leads to more winter warming and stronger variability decrease. As a result uncertainties in mean and variability response line up, with some members showing weak and others very strong warming of the cold tail of the distribution, depending on the snow albedo parametrization. The uncertainty across the ensemble regionally exceeds the CMIP3 multi-model range. Regarding summer hot extremes, the uncertainties are larger than for mean summer warming but smaller than in multi-model experiments. The summer precipitation response to a doubling of CO2 is not robust over many regions. Land surface parameter perturbations and natural variability alter the sign of the response even over subtropical regions.