Drylands are home to more than 38% of the world's population and are one of the most sensitive areas to climate change and human activities. This review describes recent progress in dryland climate change research. Recent findings indicate that the long‐term trend of the aridity index (AI) is mainly attributable to increased greenhouse gas emissions, while anthropogenic aerosols exert small effects but alter its attributions. Atmosphere‐land interactions determine the intensity of regional response. The largest warming during the last 100 years was observed over drylands and accounted for more than half of the continental warming. The global pattern and interdecadal variability of aridity changes are modulated by oceanic oscillations. The different phases of those oceanic oscillations induce significant changes in land‐sea and north‐south thermal contrasts, which affect the intensity of the westerlies and planetary waves and the blocking frequency, thereby altering global changes in temperature and precipitation. During 1948–2008, the drylands in the Americas became wetter due to enhanced westerlies, whereas the drylands in the Eastern Hemisphere became drier because of the weakened East Asian summer monsoon. Drylands as defined by the AI have expanded over the last 60 years and are projected to expand in the 21st century. The largest expansion of drylands has occurred in semiarid regions since the early 1960s. Dryland expansion will lead to reduced carbon sequestration and enhanced regional warming. The increasing aridity, enhanced warming, and rapidly growing population will exacerbate the risk of land degradation and desertification in the near future in developing countries.

How long will the four seasons be by 2100? Increasing evidence suggests that the length of a single season or in regional scales has changed under global warming, but a hemispherical‐scale response of the four seasons in the past and future remains unknown. We find that summer in the Northern Hemisphere midlatitudes has lengthened, whereas winter has shortened, owing to shifts in their onsets and withdrawals, accompanied by shorter spring and autumn. Such changes in lengths and onsets can be mainly attributed to greenhouse‐warming. Even if the current warming rate does not accelerate, changes in seasons will still be exacerbated in the future. Under the business‐as‐usual scenario, summer is projected to last nearly half a year, but winter less than 2 months by 2100. The changing seasonal clock signifies disturbed agriculture seasons and rhythm of species activities, more frequent heat waves, storms and wildfires, amounting to increased risks to humanity.

Rapid warming over the Tibetan Plateau (TP) has been associated with an increasing trend in atmospheric water vapor content, which is critical for recharging the Asian water tower. However, the mechanism associated with the wetting phenomenon remains unclear. Long‐term changes in moisture balance and precipitation (PRE) recycling processes are investigated using the ERA5 reanalysis from 1979 to 2019. The increasing trend over TP is mainly due to the summer water vapor trend with significantly increases over the western TP. Based on the moisture balance analysis, it is found that PRE, evaporation, and the convergence of moisture are all increasing over the western TP but decreasing over the eastern TP. Based on the dynamical PRE recycling model, the results suggest that both internal and external cycles contributes to the wetting over TP, with 63.87% and 36.13% contributions respectively. Further analysis found that the atmospheric heating source is also increasing over the western TP, which could shift the moisture transportation from east to west at the southern boundary of TP. The increasing moisture convergence could enhance PRE, and the enhanced latent heating in the mid‐atmosphere can further induced moisture convergence, which forms a positive feedback. However, an opposite situation occurred over the eastern TP. The internal and external cycle of the water cycle can stimulate (suppress) each other through PRE over the western (eastern) TP. This mechanism linked the changes in the PRE recycling and atmospheric circulation, and induced the increasing trend over TP in summer.

The rapid urbanization in China and the long‐range transport dust (LRTD) from arid and semi‐arid areas has resulted in an increase of PM₁₀ concentration. In this study, an interpretable deep learning model [deep forest (DF)] with FY‐4A top‐of‐the‐atmosphere reflectance (TOAR) data were used to obtain the hourly PM₁₀ in China. The optimal hourly average R² of 10‐fold cross validation can achieve 0.85 (13:00 Beijing time); The R² (RMSE, μg/m³) of the daily, monthly, and annual averages were 0.82 (24.16), 0.97 (6.53), and 0.99 (2.30), respectively. Using TOAR data, the DF model performed better than other machine learning models. The feature importance of the TOAR‐PM₁₀ model showed that TOAR and meteorological elements both contributed significantly to the model. In spring, the PM₁₀ in northern China was greater than that in southern China, which may be related to the LRTD. Excluding the dust weather periods, the areas with high PM₁₀ values in China were mainly in cities and their suburbs, where were correlated with human activities. During a dust weather process, LRTD increased PM₁₀ in northern China by 80.4%. During a mixture haze and dust weather process, the PM₁₀ increased by 130.2% in northern China, of which LRTD led to an increase of 73.7%. The sources (from the Taklimakan Desert in China) and transmission paths of these two LRTD processes were similar. The contribution of LRTD to PM₁₀ was related to dust intensity and meteorological conditions. The results showed that LRTD and local pollution to PM₁₀ was both important in haze periods.

An obvious warming trend in winter over the Tibetan Plateau (TP) in the recent decades has been widely discussed, with studies emphasizing the dominant effects of local radiative factors, including those due to black carbon (BC). The Himalayas are one of the largest snowpack- and ice-covered regions in the TP, and an ideal area to investigate local radiative effects on climate change. In this study, the coupled climate feedback response analysis method (CFRAM) is applied to quantify the magnitude of warming over the Himalayas induced by different external forcing factors and climate feedback processes. The results show that snow/ice albedo feedback (SAF) resulted in a warming of approximately 2.6°C and was the primary contributor to enhanced warming over the Himalayas in recent decades. This warming was much greater than the warming induced by dynamic and other radiative factors. In particular, the strong radiative effects of BC on the warming over the Himalayas are identified by comparing control and BC-perturbed experiments of the Community Earth System Model (CESM). As a result of strong BC effects on the Himalayas, evaporation and reduced precipitation were strengthened, accounting for local drying and land degradation, which intensified warming. These results suggest that more investigations on the local radiative effects on the climate and ecosystem are needed, especially in the high-altitude cryosphere.