标题： 降水极端情况在其原生尺度上的变化检测，源自现场测量 显示英文标题

标题： Detected changes in precipitation extremes at their native scales derived from in situ measurements

摘要： 基于地面观测站的每日累积降水——尤其是极端情况——的网格化存在困难，这是由于降水的分形特性，因此基于此类网格化日数据集的长期周期重现值及其变化的估计通常被低估。 在本文中，我们仅基于现场测量数据，对1950年至2017年间美国本土（CONUS）观测到的极端降水的高分辨率变化进行了表征。 我们的分析利用了空间统计方法，使我们能够得出不平滑极端日测量值的网格化估计，并且与原始站数据的统计结果一致，同时提高了结果的信噪比。 此外，我们使用了一种稳健的统计技术，在仔细控制假阳性率的同时，识别极端降水气候学中的显著点变化。 我们展示了极端降水统计量的季节性变化：最大的和最空间一致的点变化出现在秋季（SON），大约33%的CONUS地区显示出显著变化（绝对意义上）。 其他季节表现出很少有意义的点变化（无论是相对还是绝对意义上），这说明基于现场测量检测极端降水的点变化具有难度。 虽然我们的主要结果涉及季节性变化，但我们还展示了并讨论了极端降水统计量的年度变化。 在本文中，我们仅旨在检测时间上的变化，并将这些变化的潜在原因归因于未来的工作。 显示英文摘要

摘要： The gridding of daily accumulated precipitation -- especially extremes -- from ground-based station observations is problematic due to the fractal nature of precipitation, and therefore estimates of long period return values and their changes based on such gridded daily data sets are generally underestimated. In this paper, we characterize high-resolution changes in observed extreme precipitation from 1950 to 2017 for the contiguous United States (CONUS) based on in situ measurements only. Our analysis utilizes spatial statistical methods that allow us to derive gridded estimates that do not smooth extreme daily measurements and are consistent with statistics from the original station data while increasing the resulting signal to noise ratio. Furthermore, we use a robust statistical technique to identify significant pointwise changes in the climatology of extreme precipitation while carefully controlling the rate of false positives. We present and discuss seasonal changes in the statistics of extreme precipitation: the largest and most spatially-coherent pointwise changes are in fall (SON), with approximately 33% of CONUS exhibiting significant changes (in an absolute sense). Other seasons display very few meaningful pointwise changes (in either a relative or absolute sense), illustrating the difficulty in detecting pointwise changes in extreme precipitation based on in situ measurements. While our main result involves seasonal changes, we also present and discuss annual changes in the statistics of extreme precipitation. In this paper we only seek to detect changes over time and leave attribution of the underlying causes of these changes for future work.