South Asia is home to one of the fastest-growing populations in Asia, and human activities are leaving indelible marks on the land surface. Yet the likelihood of successive observed droughts in South Asia (SA) and its four subregions (R-1: semi-arid, R-2: arid, R-3: subtropical wet, and R-4: tropical wet and dry) remains poorly understood. Using the state-of-the-art self-calibrated Palmer Drought Severity Index (scPDSI), we examined the impact of different natural ocean variability modes on the evolution, severity, and magnitude of observed droughts across the four subregions that have distinct precipitation seasonality and cover key breadbaskets and highly vulnerable populations. The study revealed that dryness had significantly increased in R-1, R-2, and R-4 during 1981–2020. Temporal analysis revealed an increase in drought intensity for R-1 and R-4 since the 2000s, while a mixed behavior was observed in R-2 and R-3. Moreover, most of the sub-regions witnessed a substantial upsurge in annual precipitation, but a significant decrease in vapor pressure deficit (VPD) during 1981–2020. The increase in precipitation and the decline in VPD partially contributed to a significant rise in Normalized Difference Vegetation Index (NDVI) and a decrease in dryness. In contrast, a strong positive correlation was found between drought index and precipitation, and NDVI across R-1, R-2, and R-4, whereas temperature and VPD exhibited a negative correlation over these regions. No obvious link was detected with El-Niño Southern Oscillation (ENSO) events, or Indian Ocean Dipole (IOD) and drought evolution, as explored for certain regions of SA. The findings showed the possibility that the precipitation changes over these regions had an insignificant relationship with ENSO, IOD, and drought onset. Thus, the study results highlight the need for considering interactions within the longer climate system in describing observed drought risks rather than aiming at drivers from an individual perspective.

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Previous studies largely focus on changes in mean climate state and climate extremes under a warmer climate, and little is known about changes in mild weather, which is a positive and pleasant condition and is highly related to human outdoor activities. Although changes in observed mild weather frequency over China and their drivers have been revealed, the understanding of how mild weather evolves with projected warming is still limited. Here, we examine future changes in mild weather frequency over China based on comprehensive thermal comfort indices and dynamically downscaled climate projections produced by the Regional Climate Model version 4 (RegCM4) within the framework of Coordinated Regional Climate Downscaling Experiment - Coordinated Output for Regional Evaluations (CORDEX-CORE). We demonstrate that changes in mild weather frequency in a warmer future exhibit remarkable regional discrepancy. Particularly, the densely populated southeastern China will experience a robust decrease in mild weather relative to the current level, although a general increasing trend is observed in this area during the past decades. On a seasonal scale, the decrease in mild weather in summer overwhelms the increase in spring and autumn, and this is more prominent in warmer regions. For the drivers, it is suggested that changes in mild weather frequency are dominated by elevated temperatures, with little contribution from relative humidity, wind speed, and sunshine duration.

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Landfalling tropical cyclones (LTCs) is one of the most serious meteorological disasters in China due to the provided severe wind and heavy rainfall. Tropical cyclone–induced rainfall in China has been proved to decrease in recent decades. However, how landfalling tropical cyclone–induced extreme rainfall (LTCER) has changed across China, as well as the relationship between LTCER and LTCs remains poorly understood. Accordingly, the spatiotemporal distribution characteristics and long-term changes of LTCER over mainland China during the past 60 years were investigated, by using an Objective Synoptic Analysis Technique to identify LTCER. Mid and high latitudes are exposed to a greater risk of extreme rainfall from northward-moving landfalling tropical cyclones (LTCs). Meanwhile, LTCER tends to increase from 1960 to 2019 across mainland China (characterized by a decrease from 1960 to 1989 and an increase from 1990 to 2019). The LTCER trend exhibits a large spatial difference, with an increase near and to the north of 30°N, but no significant change to the south of 30°N. Moreover, the central latitude of the LTCER zone to the north of 30°N has shifted significantly southwards, while that to the south of 30°N has shifted north. Further analysis revealed that the average latitude of the LTC intensity centers to the north/south of 30°N exhibits the same shift to that of LTCER, indicating that the shift of LTCER has mainly been imposed by the migration of LTCs.

Based on different reanalysis datasets, reconstructions of East Asia landfall tropical cyclones (TCs) were compared with observations. The 20th-century reanalysis version 3 dataset (20CRv3) received the most approval in this assessment. It performed better in terms of annual frequency. The fifth generation of atmospheric reanalysis dataset (ERA5) and Japanese 55-year reanalysis dataset (JRA55) are also recommended in this study. Nevertheless, an apparent inconsistency in reconstructed TCs before and after 1980 is visible. Temporally, after the satellite era, the underestimation on TC frequency of the National Centres for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis dataset (NCAR) and 20-century reanalysis of European Center for Medium-Range Weather Forecasts (ERA20C) has been greatly improved. The downward trend of landfalling TCs is well captured by ERA5 and ERA20C. Spatially, the underestimation of TC track discrepancy is reduced in the post-satellite era. ERA5 and 20CRv3 showed relatively consistent performance compared to the former reanalysis in pre-and post-satellite time, which might be due to their better TC treatment. Despite the essential need for high resolution, this study stressed the importance of observation and assimilation development for the reanalysis TCs.