Trends and variability of extreme precipitation events are important for water‐related disaster prevention and mitigation as well as water resource management. Using daily precipitation dataset from 143 meteorological stations in the Yangtze River Basin (YRB), a suite of heavy and extreme precipitation indices recommended by the Expert Team on Climate Change Detection and Indices, which has rarely been applied in this region, were analysed during 1960–2012. Results showed that simple daily intensity index, very wet day precipitation, extremely wet day precipitation, extremely heavy precipitation days, maximum 1‐day precipitation, maximum 5‐day precipitation and maximum consecutive dry days all increased significantly during 1960–2012. In contrast, ≥10 mm precipitation days and maximum consecutive wet days decreased significantly, implying that the precipitation processes in YRB were dominated by precipitation events with shorter durations. Geographically, a wetting tendency was observed in the eastern Tibet Plateau and the middle and lower YRB, while the other regions experienced precipitation deficits. The increasing precipitation was mainly due to the intensification of extreme precipitation events and the decreasing precipitation may be attributed to the decrease of ≥10 mm precipitation days or moderate precipitation events. In addition, the regional trends were of greater magnitudes in the middle and lower YRB, indicating more frequent extreme precipitation events in these sub‐regions. Time series analysis revealed that most precipitation indices exhibited neither a stable nor a gradual pattern during 1960–2012, but a clearly upward trend, although non‐monotonous, since the late‐1980s was evident.

The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile‐copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine‐based model is compared with that of traditional data‐driven models such as the support vector regression (SVR) and the adaptive neuro‐fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations.

Having an extreme topography and heterogeneous climate, the Upper Indus Basin (UIB) is more likely to be affected by climate change and it is a crucial area for climatological studies. Based on the monthly minimum temperature (T_min), maximum temperature (T_max) and precipitation from nine meteorological stations, the spatiotemporal variability of temperature and precipitation were analyzed on monthly, seasonal, and annual scales. Results show a widespread significant increasing trend of 0.14 °C/decade for T_max, but a significant decreasing trend of −0.08 °C/decade for T_min annually, during 1955–2016 for the UIB. Seasonally, warming in T_max is stronger in winter and spring, while the cooling in T_min is greater in summer and autumn. Results of seasonal T_max indicate increasing trends in winter, spring and autumn at rates of 0.38, 0.35 and 0.05 °C/decade, respectively, while decreasing in summer with −0.14 °C/decade. Moreover, seasonal T_min results indicate increasing trends in winter and spring at rates of 0.09 and 0.08 °C/decade, respectively, while decreasing significantly in summer and autumn at rates of −0.21 and −0.22 °C/decade respectively for the whole the UIB. Precipitation exhibits an increasing trend of 2.74 mm/decade annually, while, increasing in winter, summer and autumn at rates of 1.18, 2.06 and 0.62 mm/decade respectively. The warming in T_max and an increase in precipitation have been more distinct since the mid-1990s, while the cooling in T_min is observed in the UIB since the mid-1980s. Warming in the middle and higher altitude (1500–2800 m and >2800 m) are much stronger, and the increase is more obvious in regions with elevation >2800 m. The wavelet analysis illustrated sporadic inter-annual covariance of seasonal T_max, T_min and precipitation with ENSO, NAO, IOD and PDO in the UIB. The periodicities were usually constant over short timescales and discontinuous over longer timescales. This study offers a better understanding of the local climate characteristics and provides a scientific basis for government policymakers.