Drought indices are crucial for assessing and managing water scarcity and agricultural risks; however, the lack of a unified data foundation in existing datasets leads to inconsistencies that challenge the comparability of drought indices. This study is dedicated to creating CHM_Drought, an innovative and comprehensive long-term meteorological drought dataset with a spatial resolution of 0.1° and with data collected from 1961 to 2022 in mainland China. It features six pivotal meteorological drought indices: the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), evaporative demand drought index (EDDI), Palmer drought severity index (PDSI), self-calibrating Palmer drought severity index (SC-PDSI), and vapor pressure deficit (VPD), of which the SPI, SPEI, and EDDI contain multi-scale features for periods of 2 weeks and 1–12 months. The dataset features a comprehensive application of high-density meteorological station data and a complete framework starting from basic meteorological elements (the China Hydro-Meteorology dataset, CHM). Demonstrating its robustness, the dataset excels in accurately capturing drought events across mainland China, as evidenced by its detailed depiction of the 2022 summer drought in the Yangtze River basin. In addition, to evaluate CHM_Drought, we performed consistency tests with the drought indices calculated based on Climatic Research Unit (CRU) and CN05.1 data and found that all indices had high consistency overall and that the 2-week-scale SPI, SPEI, and EDDI had potential early-warning roles in drought monitoring. Overall, our dataset bridges the gap in high-precision multi-index drought data in China, and the complete CHM-based framework ensures the consistency and reliability of the dataset, which contributes to enhancing the understanding of drought patterns and trends in China. Free access to the dataset can be found at https://doi.org/10.5281/zenodo.14634773 (Zhang and Miao, 2025).

Precipitation is a critical driver of the water cycle, profoundly influencing water resources, agricultural productivity, and natural disasters. However, existing gridded precipitation datasets exhibit markable deficiencies in capturing the spatial autocorrelation and associated environmental and climatic influences – here referred to collectively as precipitation-related covariates – that limit their accuracy, particularly in regions with sparse meteorological stations. To address these challenges, this study proposes a completely new gridded precipitation generation scheme that integrates long-term daily observations from 3746 gauges with 11 key precipitation-related covariates. Building upon the improved inverse distance weighting interpolation method used in our previous dataset CHM_PRE V1, we integrated a machine learning algorithm – light gradient boosting machine (LGBM) – to incorporate precipitation-related covariates in a data-driven manner. This integration allows for a more comprehensive characterization of precipitation patterns, jointly capturing spatial autocorrelation and covariate-based variability. By this novel scheme, a new high-precision, long-term, daily gridded precipitation dataset for the Chinese mainland (CHM_PRE V2) was developed. Validation against 63 397 high-density gauges demonstrated that CHM_PRE V2 significantly outperforms existing gridded precipitation datasets. Specifically, it achieves a mean absolute error of 1.48 mm d⁻¹ and a Kling-Gupta efficiency of 0.88, representing improvements of 12.84 % and 12.86 %, respectively, compared to the previously optimal dataset. Regarding precipitation event detection, CHM_PRE V2 achieved a Heidke skill score of 0.68 and a false alarm ratio of 0.24, surpassing the previously optimal dataset by 17.24 % and 29.17 %, respectively. These results demonstrate that CHM_PRE V2 markedly enhances precipitation measurement accuracy, reduces overestimation of precipitation events, and provides a reliable foundation for hydrological modeling and climate assessments. This dataset features a resolution of 0.1°, spans from 1960 to 2023, and will be updated annually. Free access to the dataset can be found at https://doi.org/10.11888/Atmos.tpdc.300523 (Hu and Miao, 2025).

Reliable future climate projections and water deficiency assessments require precipitation observations that are both spatially comprehensive and temporally complete, yet many global regions still suffer from observation sparsity^1,2. Here we evaluate the distribution of 221,483 internationally exchanged precipitation gauges worldwide, with records across 1900–2022, and further explore where new gauges are most needed under different scenarios. We find that at present only 13.4% of the global land surface meets the World Meteorological Organization requirements for annual precipitation monitoring, indicating widespread scarcity that has serious socioeconomic implications. Europe has the highest continental gauge density (2.4 gauges per 1,000 km²), with Germany leading among countries over 50,000 km² (22.4 gauges per 1,000 km²). Globally, 25% of land surface already requires urgent expansion of gauge networks because of climate variability, including northern South America, northern North America, Central Africa and southern Asia. Considering projected precipitation changes and socioeconomic conditions under a high-emission scenario further identifies high-need regions in India, Greenland, Bolivia and China because of climate sensitivity and socioeconomic vulnerabilities, increasing this share to 32.1% of global land. Our findings highlight important gaps in global precipitation monitoring that require strategic investments in new gauges and underscore the need for open data access.

The quasi‐global availability of satellite‐based precipitation products (SPPs) holds significant potential for improving hydrological modeling skill. However, limited knowledge exists concerning the impacts of different SPP error type on hydrological modeling skill and their sensitivity across different climate zones. In this study, forcing data sets from 10 SPPs were collected to drive hydrological models during the period 2001–2018 for 366 catchments across China. Here, we analyze the impact of the SPP errors associated with different precipitation intensities (light, moderate, and heavy) and different precipitation signatures (magnitude, variance, and occurrence) on the performance of hydrological simulations, and rank the sensitivities of SPPs errors for four major Köppen‐Geiger climate zones. The results show that heavy precipitation in SPPs is generally associated with higher errors than light and moderate precipitation when compared to gauge‐based precipitation observations, but hydrological model skill is more sensitive to errors from moderate precipitation than from heavy precipitation. The probability of moderate precipitation detection was identified as the most sensitive metric in determining hydrological model performance, with sensitivities of 0.58, 0.39, 0.59, and 0.47 in the temperate, boreal, arid, and highland climate zones, respectively. The variance error and magnitude error for heavy precipitation from SPPs were also identified as sensitive factors for hydrological modeling in the temperate and arid climate zones, respectively. These findings are crucial for enhancing the understanding of interactions between SPPs uncertainty and hydrological simulations, leading to improved data accuracy of precipitation forcing and the identification of appropriate SPPs for hydrological simulation in China.

Terrain feature points, such as the peaks and saddles, are the basic framework of surface topography and its undulations, which significantly affect the spatial distribution of surface topography. In the past, terrain feature points were extracted separately for each type, while the internal connections between the terrain feature points were ignored. Therefore, this work proposes an integrated algorithm for extracting terrain feature-point clusters, including the peaks, saddles and runoff nodes, based on the DEM data. This method includes two main processes: positive terrain-constrained ridgeline extraction and terrain feature-point cluster extraction. Firstly, a threshold determination method of flow accumulation in the hydrological analysis is proposed by combining morphological characteristics with runoff simulation, and the ridgelines are extracted based on this threshold. Subsequently, the peaks and their control areas are extracted by space segmentation. Meanwhile, the saddles and runoff nodes are obtained by spatial intersection. Finally, the integrated terrain feature-point clusters are obtained by merging the three extracted terrain feature points. This method was experimented with in the six typical sample areas in Shaanxi Province and verified its results by contour lines and optical images. It shows that the spatial positions of the extracted terrain feature clusters are accurate, and the coupling relationships are great. Finally, the experiments show that the statistical attributes of point clusters and their spatial distribution trends have an obvious correlation with geomorphic types and geomorphic zoning, which can provide an important reference for geomorphic zoning and mapping.