Climate variation in Central Asia is examined using changes of climate classification to obtain geographically specific information of the variation across the region. Major results show the northward expansion of desert climate by over 100 km in mid‐latitudes of the region since the 1980s. In the meantime, all types of climates in the region have increased temperatures. In mountainous areas, previous cold climate is replaced by warmer and also wetter climate. These changes could have largely resulted in the fast retreat of glaciers and the temporary rise of groundwater and the water levels of lakes in the drainage areas in recent decades. Because different climate types are associated with specific flora and fauna, the shifts of the climate types in locations have elevated the potential to initiate new feedbacks of the ecological systems to the climate and complicate its variation.

Reliable long-term runoff prediction for Wudongde and Three Gorges reservoirs, two major reservoirs in the upper Yangtze River basin, is crucial for optimal operation of cascade reservoirs and hydropower generation planning. This study develops a data-driven model that integrates large-scale climate factors with a Gated Recurrent Unit (GRU) neural network to enhance runoff forecasting at lead times of 7–18 months. Key climate predictors were systematically selected using correlation analysis and stepwise regression before being fed into the GRU model. Evaluation results demonstrate that the proposed model can skillfully predict the variability and magnitude of reservoir inflow. For Wudongde Reservoir, the model achieved a mean correlation coefficient (CC) of 0.71 and Kling–Gupta Efficiency (KGE) of 0.57 during the training period, and values of 0.69 and 0.53 respectively during the testing period. For Three Gorges Reservoir, the CC was 0.67 (training) and 0.66 (testing), and the KGE was 0.52 and 0.49 respectively. The model exhibited robust forecasting capabilities across a range of lead times but showed distinct seasonal variations, with superior performance in summer and winter compared to transitional months (April and October). This framework provides a valuable tool for long-term runoff forecasting by effectively linking large-scale climate signals to local hydrological responses.

Deep convective systems (DCSs) play a crucial role in global water cycles, energy distribution, and extreme weather events. This study aims to enhance the understanding of DCSs by creating a comprehensive dataset through the fusion of multisource A-Train satellite observations and reanalysis data. Fusing data from multiple active and passive sensors allows us to capture detailed vertical profiles and life stages of DCSs, overcoming the limitations of polar-orbiting satellites in tracking convection over time. Comparison with convection tracking data from geostationary satellites confirmed the reliability of lifecycle determination. Reanalysis data are included for each observed DCS samples, incorporating environmental conditions and aerosol data up to 36 h before the DCS occurrence. Using the fused dataset, we examined the radiative properties of DCSs across different stages of their lifecycle and the influence of environmental factors and aerosols on them. Radiative heating rates showed distinct variations, with mature stages exhibiting the highest shortwave heating and longwave cooling rates due to denser and higher cloud tops. Our findings reveal the role of humidity in increasing cloud top heights and the influence of vertical wind shear on convection development. Additionally, aerosol impacts were notable during the mature and dissipating stages, with higher concentrations linked to increased cloud top heights and lower temperatures. This comprehensive dataset advances the understanding of DCS dynamics, aiding in the improvement of predictive models for severe weather.

Orographic cirrus clouds frequently occur over mountainous regions and can influence the radiative balance of the upper troposphere, yet their characteristics and regional variability remain insufficiently understood on a global scale. In this study, we investigate the occurrence, vertical structure, and microphysical and optical properties of orographic cirrus over four major mountainous regions, namely the Rocky Mountains, the Andes, the Alps, and the Himalayas, using the Identification and Classification of Cirrus (IC-CIR) framework together with satellite observations from MODIS, CloudSat, and CALIPSO. The results reveal clear regional differences in both occurrence and structure. Cloud cover is higher over the Himalayas and the Alps and lower over the Andes, while seasonal variability is strongest over the Himalayas and the Alps and weakest over the Andes. In terms of vertical structure, cirrus over the Andes reaches higher cloud tops and exhibits a bimodal distribution. The Andes also show smaller values of ice water path, optical depth, and cirrus reflectance. These results provide a unified comparison of orographic cirrus clouds across four representative major mountainous regions and highlight substantial regional differences in their characteristics and potential radiative influence under different topographic and dynamical environments.