ABSTRACT: We systematically examined past spatiotemporal changes in climate variability to gain some cross-regional insights into South Asia’s vulnerability to extreme conditions. Gridded Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) precipitation and Princeton Global Meteorological Forcing Dataset (PRINCETON) temperature data from 1975-2004 were used to derive a suite of annual extreme indices. Long-term mean and decadal variations of these indices were mapped. Long-term change tendencies were also detected from a suite of ‘slope’ maps composed by the 30 yr change trend at each grid cell in the region. Most precipitation indices indicated a tendency towards drier conditions, whereas all temperature indices marked a steady coherent warming trend. The extremely wet day precipitation index exhibited the largest change, indicating an increase in heavy precipitation in South Asia. The highest maximum temperature extreme showed increases, indicating more unbearable heatwaves in the region. These trends present a previously unrecognised regional picture of the patterns and trends in historical climate extremes, with each grid cell representing spatiotemporal characteristics of changes. The present study is superior to most studies that only summarise an averaged regional trend from tendencies over large areas, and therefore will improve trans-boundary understanding of extreme climates in South Asia. Our study also exemplifies the application of existing gridded regional/global data sets. It provides valuable means of cross-regional information for bridging gaps where gauging observations are unavailable, particularly in data-poor developing countries.

中文翻译：

---

过去几十年南亚气候极端事件的时空变化特征

摘要：我们系统地研究了过去气候变化的时空变化，以期获得对南亚极度脆弱性的跨地区见解。利用1975-2004年栅格化的亚洲降水-高度分辨的观测资料综合向评估（APHRODITE）降水和普林斯顿全球气象强迫数据集（PRINCETON）的温度数据得出了一系列年度极端指数。绘制了这些指数的长期均值和年代际变化。还从该地区每个网格单元的30年变化趋势组成的一系列“坡度”图中检测到长期变化趋势。多数降水指数表明天气趋于干燥，而所有温度指数均表明气候持续稳定变暖。极端湿润的日降水指数变化最大，表明南亚的强降水增加。最高的最高温度极端升高，表明该地区更多无法忍受的热浪。这些趋势提供了历史气候极端事件的模式和趋势的以前无法识别的区域图，每个网格代表变化的时空特征。本研究优于大多数仅从大面积趋势中总结出平均区域趋势的研究，因此将提高对南亚极端气候的跨界认识。我们的研究还例证了现有网格化区域/全球数据集的应用。