Reference precipitation (RP) serves as a benchmark for evaluating the accuracy of precipitation products; thus, the selection of RP considerably affects the evaluation. In order to quantify this impact and provide guidance for RP selection, three interpolation methods, namely inverse distance weighting (IDW), ordinary kriging (OK), and geographical weighted regression (GWR), along with six groups of station densities, were adopted to generate different RPs, based on the super-high-density rainfall observations as true values, and we analyzed the errors of different RPs and the impacts of RP selection on the assessment of GPM precipitation products. Results indicate that the RPs from IDW and GWR both approached the true values as the station density increased (CC > 0.90); while the RP from OK showed some differences (CC < 0.80), it was similar to GWR when the station density was low, but the accuracy improved at first and then worsened as the station density continued to increase; the evaluation results based on different RPs showed remarkable differences even under the same conditions; when the average distance between rainfall gauges that were utilized to generate RPs was below the medium value (i.e., d < 20 km), the evaluation based on RP derived from IDW and GWR was close enough to that based on the true precipitation, which indicates its feasibility in evaluating satellite precipitation products.

In recent years, global climate change causes more extreme precipitation events in Lancang-Mekong River Basin (Lancang-Mekong Basin). It leads to the increase of rainstorm and flood risk, which poses a threat to the flood control safety of Lancang-Mekong Basin. Based on the precipitation data of four global climate models of CMIP6, this paper selected six indexes, including PRCPTOT and R10, to study the change characteristics of the extreme precipitation indexes in the Lancang-Mekong Basin during 1980–2020 and predicted the development trend of the extreme precipitation indexes in the Lancang-Mekong Basin during 2021–2050. The results show that from 1980 to 2020, the extreme precipitation events in regions I and IV showed an increasing trend, while those in regions II and III showed a decreasing trend. From 2021 to 2050, the extreme precipitation events in all regions of the Lancang-Mekong River show an upward trend, and most indexes increased compared with the historical period. The extreme precipitation indexes in most regions increases the most under the scenario of SSP5-8.5, while the increase is small under the scenario of SSP2-4.5; The six extreme precipitation indexes selected in the paper can better reflect the trend of extreme precipitation events and floods. The trend of extreme precipitation indexes in the Lancang-Mekong River basin in the future is more significant, which indicates that it may be affected more frequently by extreme precipitation events and floods.

Long-term and high-resolution reanalysis precipitation datasets provide important support for research on climate change, hydrological forecasting, etc. The comprehensive evaluation of the error performances of the newly released ERA5-Land and CRA40-Land reanalysis precipitation datasets over the Yongding River Basin in North China was based on the two error decomposition schemes, namely, decomposition of the total mean square error into systematic and random errors and decomposition of the total precipitation bias into hit bias, missed precipitation, and false precipitation. Then, the error features of the two datasets and precipitation intensity and terrain effects against error features were analyzed in this study. The results indicated the following: (1) Based on the decomposition approach of systematic and random errors, the total error of ERA5-Land is generally greater than that of CRA40-Land. Additionally, the proportion of random errors was higher in summer and over mountainous areas, specifically, the ERA5-Land accounts for more than 75%, while the other was less than 70%; (2) Considering the decomposition method of hit, missed, and false bias, the total precipitation bias of ERA5-Land and CRA40-Land was consistent with the hit bias. The magnitude of missed precipitation and false precipitation was less than the hit bias. (3) When the precipitation intensity is less than 38 mm/d, the random errors of ERA5-Land and CRA40-Land are larger than the systematic error. The relationship between precipitation intensity and hit, missed, and false precipitation is complicated, for the hit bias of ERA5-L is always smaller than that of CRA40-L, and the missed precipitation and false precipitation are larger than those ofCRA40-L when the precipitation is small. The error of ERA5-Land and CRA40-Land was significantly correlated with elevation. A comprehensive understanding of the error features of the two reanalysis precipitation datasets is valuable for error correction and the construction of a multi-source fusion model with gauge-based and satellite-based precipitation datasets.

Adjacent mountain area and plain area generally belong to the same climate zone, and a close hydraulic connection is also found between them. However, the flood controls in these two areas belong to different management departments. Flood occurring in these two areas can bring serious waterlog disasters, posing great threat to human lives and property. Previous studies mainly focused on the individual flood event in mountain or plain areas, while the research on the floods simultaneously occurring in both areas is insufficient. Hence, this study establishes a copula function-based assessment method to systematically analyze the characteristics of simultaneously-occurring floods in mountain and plain areas in Hangzhou City, thereby providing a basis for the collaborative management of floods in both areas. Results show that the proposed approach can give water level combinations in mountain and plain areas that are prone to co-occurring floods. Moreover, the water level in plain area is most likely to simultaneously appear with the water level in mountain areas under similar return periods, indicating that floods of equivalent level are likely to occur in plain areas when severe floods occur in mountain areas. This study provides a better understanding on the interplay of floods simultaneously occurring in mountain and plain areas, offering insights for coordinating the flood protection system in these two areas.

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period.