In the Asia–Pacific region (APR), extreme precipitation is one of the most critical climate stressors, affecting 60% of the population and adding pressure to governance, economic, environmental, and public health challenges. In this study, we analyzed extreme precipitation spatiotemporal trends in APR using 11 different indices and revealed the dominant factors governing precipitation amount by attributing its variability to precipitation frequency and intensity. We further investigated how these extreme precipitation indices are influenced by El Niño-Southern Oscillation (ENSO) at a seasonal scale. The analysis covered 465 ERA5 (the fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts) study locations over eight countries and regions during 1990–2019. Results revealed a general decrease indicated by the extreme precipitation indices (e.g., the annual total amount of wet-day precipitation, average intensity of wet-day precipitation), particularly in central-eastern China, Bangladesh, eastern India, Peninsular Malaysia and Indonesia. We observed that the seasonal variability of the amount of wet-day precipitation in most locations in China and India are dominated by precipitation intensity in June–August (JJA), and by precipitation frequency in December–February (DJF). Locations in Malaysia and Indonesia are mostly dominated by precipitation intensity in March–May (MAM) and DJF. During ENSO positive phase, significant negative anomalies in seasonal precipitation indices (amount of wet-day precipitation, number of wet days and intensity of wet-day precipitation) were observed in Indonesia, while opposite results were observed for ENSO negative phase. These findings revealing patterns and drivers for extreme precipitation in APR may inform climate change adaptation and disaster risk reduction strategies in the study region.

Due to the large amount of water resources stored in glaciers, permafrost, and lakes, the source region of the Yangtze River (SRYR) is of great importance for the overall basin water flow. For this purpose, a state of art review and calculations were made for the period 1957–2013 using observed hydrological and meteorological data with a water balance approach. Actual evapotranspiration was calculated and validated by empirical formulas. Water storage change analysis was conducted with uncertainty boundaries using a 10-year moving window. Results show that temperature, precipitation, and actual evapotranspiration in the SRYR increased by 0.34 °C, 11.4 mm, and 7.6 mm per decade, respectively (significant at 0.05 probability level). Runoff appears to have increased at a rate of 3.3 mm per decade. The SRYR water storage in total has not changed significantly during the period, although the moving average is mostly below zero. Based on the water balance equation, the increase in calculated evapotranspiration is mainly due to the significantly increasing temperature. This in combination with increasing precipitation leads to a relatively stable water storage during the study period. Correlation analyses show that precipitation dominates runoff during the warm season (May to October), while temperature anomalies dominate the runoff during the cold season (November to April). The influence of temperature on runoff seems to enhance during the winter period.

Precipitation extremes and their underlying causes are important processes to understand to plan appropriate adaptation measures. This paper presents an analysis of the spatiotemporal variability and trend of precipitation extremes in the important source region of the Yellow River and explores the connection to global teleconnection patterns and the 850-mb vector wind. Six indices for precipitation extremes were computed and analyzed for assessment of a changing regional climate. Results showed that these indices have a strong gradient from the northwest to the southeast part for the period 1961–2015, due to the great influence from the south-easterly summer monsoon flow. However, no statistically significant trends were found for the defined indices at the majority of stations, and their spatial distribution are noticed by irregularly mixed positive and negative changes except for the maximum number of consecutive wet days (CWD). Singular value decomposition analysis revealed that the precipitation extreme indices—including annual total precipitation when daily precipitation >95th percentile (R95p), annual count of days with daily precipitation ≥10 mm (R10mm), annual maximum consecutive 5-day precipitation (R5d), total precipitation divided by the number of wet days (SDII), and CWD—are negatively related to the El Nino-Southern Oscillation (NINO 3.4) in the first mode, and the maximum number of consecutive dry days (CDD) is positively related to the Scandinavian pattern in the second mode at 0.05 significance level. The 850-mb vector wind analysis showed that the southwestern monsoon originating from the Indian Ocean brings sufficient moisture to this region. Furthermore, the anti-cyclone in the western part of the North Pacific plays a significant role in the transport of moisture to the source region of the Yellow River. The links between precipitation extremes and teleconnection patterns explored in this study are important for better prediction and preparedness of climatic extremes.

Monthly precipitation data at regular grids of 0.5° × 0.5° derived from observations during June‐August 1961‐2016 by the China Meteorological Administration were used to reveal characteristics of large‐scale circulations associated with rainy season precipitation over the source region of the Yangtze River (SRYR). The integrated impact of major influencing circulation patterns was examined by composites. Results showed that the first rainy season precipitation mode associates with the Southern Oscillation Index (SOI) and the Pacific Decadal Oscillation (PDO), explaining 64% of spatial and temporal rainy season precipitation variance in the region. Composites of precipitation pattern under different phases of SOI and PDO revealed that the effect of PDO on precipitation varies with the SOI phase. When in phase with the SOI, PDO‐induced precipitation anomalies are magnified with as quantified by the canonical correlation pattern. When out‐of‐phase, anomalies weaken or even disappear. Composites of moisture flux patterns show that large‐scale atmospheric circulation affects the strength of westerlies that transport moisture to the study area and formation of convergence. In coming decades, the PDO is likely to continue in a negative phase with La Niña (positive SOI) events, implying more precipitation during the rainy season. Consequently, this knowledge can be used to improve decision making regarding water supply and flood risk management in the SRYR.

Life-history theory predicts that organisms inhabiting harsh environments such as high altitudes should invest less in reproduction and more in survival. Testis size is associated with the intensity of male-male competition for mating and thus may be treated as an indicator of male reproductive investment. Hence, it may be expected that organisms will reduce their testis size with increasingly harsh environments. Here we test this prediction in a toad species, Scutiger boulengeri, endemic to the Tibetan plateau using data from three populations located at altitudes of 4078, 4276, and 4387 m. Consistent with the prediction, male toads exhibited smaller testes at higher altitudes, despite the relatively narrow altitudinal span. It is likely that cold climates and strong seasonality constrain the ability of high-altitude male toads to allocate more energy into reproduction, thereby leading to small testis size. In addition, the left testis was significantly heavier than the right one and the degree of size asymmetry was unrelated to either altitude or body condition.

This paper developed a multi-space prediction model for seasonal precipitation using a high-resolution grid dataset (0.5° × 0.5°) together with climate indices. The model is based on principal component analyses (PCA) and artificial neural networks (ANN). Trend analyses show that mean annual and seasonal precipitation in the area is increasing depending on spatial location. For this reason, a multi-space model is especially suited for prediction purposes. The PCA-ANN model was examined using a 64-grid mesh over the source region of the Yangtze River (SRYR) and was compared to a traditional multiple regression model with a three-fold cross-validation method. Seasonal precipitation anomalies (1961–2015) were converted using PCA into principal components. Hierarchical lag relationships between principal components and each potential predictor were identified by Spearman rank correlation analyses. The performance was compared to observed precipitation and evaluated using mean absolute error, root mean squared error, and correlation coefficient. The proposed PCA-ANN model provides accurate seasonal precipitation prediction that is better than traditional regression techniques. The prediction results displayed good agreement with observations for all seasons with correlation coefficients in excess of 0.6 for all spatial locations.