The precipitation concentration index (PCI) is a powerful indicator for temporal precipitation distribution and is also very useful for the assessment of seasonal precipitation changes. The primary objectives of this study are to investigate and analyse the temporal–spatial variability patterns of annual and seasonal PCI values based on monthly precipitation data. These data were collected from 597 meteorological stations located throughout China, for the time period of 1960–2016, and were used to assess the impacts of geographical parameters (latitude, longitude, and altitude) on the PCI. Additionally, the possible teleconnection with the large‐scale circulation pattern was investigated. Our results reveal that the variation trend of annual PCI values has decreased significantly at a rate of −.234/10 year (α = .01) in China over the past 57 years. For all studied station records, 434 (72.7%) stations showed decreasing trends of PCI values, and these stations are distributed over large areas in China. On an annual scale, the average PCI value ranged from 11 in Hunan province to 44 in Qinghai province. The precipitation concentration in China can be described as strongly irregular in the western and northern parts of the northwest and in the northern region of the Tibetan Plateau, while it is irregular in the southwest and the north of China, and moderately irregular in some parts of the middle‐lower regions of the Yangtze River and southern China. The regularity of the annual precipitation pattern significantly decreased in spring, autumn, and winter from southeastern to northwestern China, and was the most in winter. However, the summer precipitation dispersion and the pattern in the considered period were more regular than those of the other seasons. Furthermore, changes in the PCI appear to be rather complex and possibly related to global atmospheric characteristics as well as geographical factors (latitude, longitude, and altitude). The results presented in this study indicate that the PCI is an essential feature for water resource planning, prediction of risk due to droughts or floods, and the management of natural resources.

High spatial resolution measurements from the Advanced Very High Resolution Radiometer (AVHRR) on the Meteorological Operation (MetOp)-A satellite that are collocated to the footprints from the Infrared Atmospheric Sounding Interferometer (IASI) on the satellite are exploited to improve and quality control cloud-cleared radiances obtained from the IASI. For a partial set of mostly ocean MetOp-A orbits collected on 3 October 2010 for latitudes between 70°S and 75°N, these cloud-cleared radiances and clear-sky subpixel AVHRR measurements within the IASI footprint agree to better than 0.25-K root-mean-squared difference for AVHRR window channels with almost zero bias. For the same dataset, surface skin temperatures retrieved using the combined AVHRR, IASI, and Advanced Microwave Sounding Unit (AMSU) cloud-clearing algorithm match well with ECMWF model surface skin temperatures over ocean, yielding total uncertainties ≤1.2 K for scenes with up to 97% cloudiness. AbstractHigh spatial resolution measurements from the Advanced Very High Resolution Radiometer (AVHRR) on the Meteorological Operation (MetOp)-A satellite that are collocated to the footprints from the Infrared Atmospheric Sounding Interferometer (IASI) on the satellite are exploited to improve and quality control cloud-cleared radiances obtained from the IASI. For a partial set of mostly ocean MetOp-A orbits collected on 3 October 2010 for latitudes between 70°S and 75°N, these cloud-cleared radiances and clear-sky subpixel AVHRR measurements within the IASI footprint agree to better than 0.25-K root-mean-squared difference for AVHRR window channels with almost zero bias. For the same dataset, surface skin temperatures retrieved using the combined AVHRR, IASI, and Advanced Microwave Sounding Unit (AMSU) cloud-clearing algorithm match well with ECMWF model surface skin temperatures over ocean, yielding total uncertainties ≤1.2 K for scenes with up to 97% cloudiness.

Analysis of variations in 12 extreme temperature indices at 68 meteorological stations on the Yunnan-Guizhou Plateau (YGP) in southwestern China during 1960–2019 revealed widespread significant changes in all temperature indices. The temperature of the hottest days and coldest nights show significantly increasing trends, and the frequencies of the warm days and nights also present similar trends. The temperature of the coldest night has a significant and strong warming trend (0.38 °C/decade), whereas the frequency of frost days shows the fastest decrease (1.5 days/decade). Increases in the summer days are statistically significant, while a decreasing trend for the diurnal temperature range is not significant. Furthermore, there were significant differences in the changes of temperature indices between 1960–1989 and 1990–2019. Most parts of the YGP underwent significant warning, manifesting that the mountainous regions are relatively sensitive and vulnerable to climate change. The correlation coefficients between the temperature indices and various geographical factors (latitude, longitude, and height) reflect the complexity of regional temperature variability and indicate enhanced sensitivity of extreme temperatures to geographical factors on the YGP. It was also found that extreme temperatures generally had weaker correlations with the El Nino-Southern Oscillation, North Pacific Index, Southern Oscillation Index, North Atlantic Oscillation, and East Asian Summer Monsoon Index than with the South Asian summer monsoon index, Nino4 indices and Arctic Oscillation, and there were more insignificant correlations. Regional trends of the extreme temperature indices reflect the non-uniform temperature change over the YGP, which is due to the complex interaction between atmospheric circulation patterns and local topography. The results of this study have important practical significance for mitigating the adverse effects of extreme climatic changes, in particular for the YGP with its typical karst geomorphology and fragile ecological environment.

Precipitation, essential for the water cycle and key to surface runoff and groundwater, causes floods and droughts when unevenly distributed. Understanding the variations in precipitation across China is vital for managing water resources and preventing weather‐related disasters. In this study, we analyzed the spatial–temporal variations in rainfall amounts and the number of rainy days across different levels in China using daily precipitation data during 1960–2019. We found a nonsignificant increase in annual total precipitation (ATP), but a significant decline in the number of days with ATP during this period. This shift suggests that precipitation is becoming more concentrated in fewer days, potentially due to an increase in the frequency of heavy rain (25 ≤ p < 50 mm/day, L₃), rainstorm (50 ≤ p < 100 mm/day, L₄), and heavy rainstorm (p > 100 mm/day, L₅). The amount and frequency of precipitation in light rain (0.1 ≤ p < 10 mm/day, L₁) and moderate rain (10 ≤ p < 25 mm/day, L₂) exhibited a decreasing trend during this period, whereas the patterns for L₃, L₄, and L₅ demonstrated an increasing trend. Notably, the decrease in the number of days with L₁ and L₂ precipitation was relatively minor compared with the substantial increase in the number of days experiencing L₃, L₄, and L₅ precipitation. Despite L₁ precipitation making up only 24.9% of China's ATP, it accounts for 78.6% of total precipitation days. This underscores the important role played by L₁ precipitation events in determining the overall frequency of precipitation occurrences in China. Significant regional disparities are observed in both precipitation amounts and the number of precipitation days across different precipitation levels. Furthermore, large‐scale climate indices have consistently affected China's precipitation patterns since 1960, impacting not just the current year but possibly extending into the subsequent year.