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Recent studies show variations in precipitation‐gridded data set accuracy with changing geographical parameters. Ensemble precipitation products, combining diverse data sets, offer global‐scale effectiveness, but applying them to regional studies, particularly in small to medium‐sized sub‐basins, presents challenges in addressing precipitation dependence on specific geographical conditions. Here, we present a newly developed Clusters Based‐Minimum Error approach to assimilate different open‐source gridded precipitation data sets for forming an accurate precipitation product over small to medium‐sized hilly terrain basins, with limited precipitation gauges. This methodology generates the New Gridded Precipitation Data Set (NGPD) from 1991 to 2022 for the Upper Ganga Basin in the western Himalaya, covering approximately 22,292 km2. The study utilizes nine open‐source gridded precipitation data sets and 11 observed precipitation gauges, NGPD is evaluated through station‐wise, grid‐wise, and elevation‐wise analyses using statistical parameters, quantile‐quantile plots, daily coefficient of determination, Rainfall Anomaly Index, and seasonality/precipitation pattern analyses. Results demonstrate the superior performance of NGPD compared to other gridded precipitation sources across various evaluation metrics. Nash‐Sutcliffe Efficiency (NSE), Coefficient of determination (R2), and Root mean squared error (RMSE) range from 0.67 to 0.90, 0.73–0.93, and 4.4–10.69 mm/day, respectively, w.r.t 11 observed precipitation gauges. NGPD outperforms the widely used IMD data set in India, exhibiting a monthly scale improvement of 18.47% and 17.7% in average NSE and R2 values, respectively. Additionally, the methodology is also successfully applied to the Tamor Basin in Nepal, proving its reliability for various Himalayan regions. This approach reliably creates accurate gridded precipitation data sets for hilly sub‐basins, especially in Himalayan regions with limited station data. Key Points A cluster‐based data assimilation approach to develop accurate gridded precipitation data in the Himalayan basins Consideration of topographic and climatic parameters to identify homogenous rainfall clusters to incorporate precipitation change Multi‐level evaluation of the newly developed gridded precipitation w.r.t. observed and open sources global gridded precipitation data sets

The outcome of data analysis depends on the quality and completeness of data. This paper considers various techniques for filling in missing precipitation data. To assess suitability of the different methods for filling in missing data, monthly precipitation data collected at six different stations was considered. The complete sets (with no missing values) are used to predict monthly precipitation. The arithmetic averaging method, the multiple linear regression method, and the non-linear iterative partial least squares algorithm perform best. The multiple regression method provided a successful estimation of the missing precipitation data, which is supported by the results published in the literature. The multiple imputation method produced the most accurate results for precipitation data from five dependent stations. The decision-tree algorithm is explicit, and therefore it is used when insights into the decision making are needed. Comprehensive error analysis is presented.

Due to its complex and diverse terrain, precipitation gauges in the Tibetan Plateau (TP) are sparse, making it difficult to obtain reliable precipitation data for environmental studies. Data merging is a method that can integrate precipitation data from multiple sources to generate high-precision precipitation data. However, the more commonly used methods, such as regression and machine learning, do not usually consider the local correlation of precipitation, so that the spatial pattern of precipitation cannot be reproduced, while deep learning methods do incorporate spatial correlation. To explore the ability of using deep learning methods in merging precipitation data for the TP, this study compared three methods: a deep learning method—a convolutional neural network (CNN) algorithm, a machine learning method—an artificial neural network (ANN) algorithm, and a statistical method based on Extended Triple Collocation (ETC) in merging precipitation from multiple sources (gauged, grid, satellite and dynamic downscaling) over the TP, as well as their performance for hydrological simulations. Dynamic downscaling data driven by global reanalysis data centered on the TP were introduced in the merging process to better reflect the spatial variability of precipitation. The results show that: (1) in terms of the meteorological metrics, the merged data perform better than the gauge interpolation data. By using data merging, the error between the raw multi-source and gauged precipitation can be reduced, and the precipitation detection capability can be greatly improved; (2) The merged precipitation data also perform well in the hydrological evaluation. The Xin’anjiang (XAJ) model parameter calibration experiments at the source of the Yangtze River (SYR) and the source of the Yellow River (SHR) were repeated 300 times to remove uncertainty in the model parameter results. The median Kling-Gupta Efficiency Coefficients (KGE) of simulated runoff from the merged data of the ANN, CNN and ETC methods for the SYR and the SHR are 0.859, 0.864, 0.838 and 0.835, 0.835, 0.789, respectively. Except for the ETC merging data at the SHR, the performance of other merged data was improved compared to the simulation results of the gauged precipitation (KGE=0.807 at the SYR, KGE=0.828 at the SHR); and (3) In contrast to the machine learning ANN method and the statistical ETC method, the deep learning method, CNN, consistently showed better performance.

This study explores the variations in Indian summer monsoon rainfall (ISMR) and its substantial influence on India’s economic and ecological landscape. The research particularly focuses on the relationship between ISMR and several distant monsoon indices, including the “Indian Summer Monsoon Index (ISMI), East Asian Summer Monsoon Index (EASMI), South Asian Summer Monsoon Index (SASMI), and Western–North Pacific Monsoon Index (WNPMI).” Spanning from 1948 to 2017, the study uses high‐resolution gridded precipitation data to analyze correlations, coherence patterns, and teleconnections between ISMR and these indices. To capture the statistical characteristics of the monsoon, methodologies such as Spearman’s rank correlation, the Mann–Kendall trend test, Sen’s slope estimator, and wavelet transform coherence (WTC) analysis were applied. Results indicate that SASMI ( ρ = 0.539) stands out as a robust alternative long‐distance indicator, exhibiting stronger correlation with ISMR after ISMI ( ρ = 0.709). Notably, the Himalayan (Him) and North‐Eastern (NE) regions demonstrated heightened sensitivity to SASMI’s influence. The study further reveals a significant decline in monsoonal precipitation across India, with an average reduction of 0.652 mm/year. Drying trends are particularly pronounced in regions such as the Him (−0.976 mm/year; −0.131%/year), NE (−1.281 mm/year; −0.113%/year), West‐Central (WC; −0.772 mm/year; −0.074%/year), and Peninsular (Pen; −0.317 mm/year; −0.043%/year) areas. Conversely, a modest wetting trend (0.187 mm/year) is observed in the North‐Western (NW) region. Regional variations in rainfall patterns persist, with increased precipitation evident in the Western Ghats, WC, NE, and Eastern India, while other parts experience reduced rainfall. An essential finding of the study is the previously underestimated influence of SASMI on ISMR. Between 1971 and 2006, weak coherence patterns suggest a diminishing association between ISMR and ISMI, potentially attributed to climatic fluctuations and the overarching effects of climate change.

At present, high-resolution drought indices are scarce, and this problem has restricted the development of refined drought analysis to some extent. This study explored the possibility of calculating the standardized precipitation index (SPI) with short-term precipitation sequences in China, based on data from 2416 precipitation observation stations covering the time period from 1961 to 2019. The result shows that it is feasible for short-sequence stations to calculate SPI index, based on the spatial interpolation of the precipitation distribution parameters of the long-sequence station. Error analysis denoted that the SPI error was small in east China and large in west China, and the SPI was more accurate when the observation stations were denser. The SPI error of short-sequence sites was mostly less than 0.2 in most areas of eastern China and the consistency rate for the drought categories was larger than 80%, which was lower than the error using the 30-year precipitation samples. Further analysis showed that the estimation error of the distribution parameters β and q was the most important cause of SPI error. Two drought monitoring examples show that the SPI of more than 50,000 short-sequence sites can correctly express the spatial distribution of dry and wet and have refined spatial structure characteristics.

Based on hourly precipitation data in eastern China in the warm season during 1961-2000,spatial distributions of frequency for 20 mm h 1 and 50 mm h 1 precipitation were analyzed,and the criteria of short-duration rainfall events and severe rainfall events are discussed.Furthermore,the percentile method was used to define local hourly extreme precipitation;based on this,diurnal variations and trends in extreme precipitation were further studied.The results of this study show that,over Yunnan,South China,North China,and Northeast China,the most frequent extreme precipitation events occur most frequently in late afternoon and/or early evening.In the Guizhou Plateau and the Sichuan Basin,the maximum frequency of extreme precipitation events occurs in the late night and/or early morning.And in the western Sichuan Plateau,the maximum frequency occurs in the middle of the night.The frequency of extreme precipitation （based on hourly rainfall measurements） has increased in most parts of eastern China,especially in Northeast China and the middle and lower reaches of the Yangtze River,but precipitation has decreased significantly in North China in the past 50 years.In addition,stations in the Guizhou Plateau and the middle and lower reaches of the Yangtze River exhibit significant increasing trends in hourly precipitation extremes during the nighttime more than during the daytime.

This study examined the trends in annual rainfall and temperature extremes over the Vea catchment for the period 1985–2016, using quality-controlled stations and a high resolution (5 km) Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. The CHIRPS gridded precipitation data’s ability in reproducing the climatology of the catchment was evaluated. The extreme rainfall and temperature indices were computed using a RClimdex package by considering seventeen (17) climate change indices from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI). Trend detection and quantification in the rainfall (frequency and intensity) and temperature extreme indices were analyzed using the non-parametric Mann–Kendall (MK) test and Sen’s slope estimator. The results show a very high seasonal correlation coefficient (r = 0.99), Nash–Sutcliff efficiency (0.98) and percentage bias (4.4% and −8.1%) between the stations and the gridded data. An investigation of dry and wet years using Standardized Anomaly Index shows 45.5% frequency of drier than normal periods compared to 54.5% wetter than normal periods in the catchment with 1999 and 2003 been extremely wet years while the year 1990 and 2013 were extremely dry. The intensity and magnitude of extreme rainfall indices show a decreasing trend for more than 78% of the rainfall locations while positive trends were observed in the frequency of extreme rainfall indices (R10mm, R20mm, and CDD) with the exception of consecutive wet days (CWD) that shows a decreasing trend. A general warming trend over the catchment was observed through the increase in the annual number of warm days (TX90p), warm nights (TN90p) and warm spells (WSDI). The spatial distribution analysis shows a high frequency and intensity of extremes rainfall indices in the south of the catchment compared to the middle and northern of part of the catchment, while temperature extremes were uniformly distributed over the catchment.

Drought is a natural phenomenon that has adverse effects on agriculture, the economy, and human well-being. The primary objective of this research was to comprehensively understand the drought conditions in Sistan and Balouchestan Province from 2002 to 2017 from two perspectives: vegetation cover and hydrology. To achieve this goal, the study utilized MODIS satellite data in the first part to monitor vegetation cover as an indicator of agricultural drought. In the second part, GRACE satellite data were employed to analyze changes in groundwater resources as an indicator of hydrological drought. To assess vegetation drought, four indices were used: Vegetation Health Index (VHI), Vegetation Drought Index (VDI), Visible Infrared Drought Index (VSDI), and Temperature Vegetation Drought Index (TVDI). To validate vegetation drought indices, they were compared with Global Land Data Assimilation System (GLDAS) precipitation data. The vegetation indices showed a strong, statistically significant correlation with GLDAS precipitation data in most regions of the province. Among all indices, the VHI showed the highest correlation with precipitation (moderate (0.3–0.7) in 51.7% and strong (≥0.7) in 45.82% of lands). The output of vegetation indices revealed that the study province has experienced widespread drought in recent years. The results showed that the southern and central regions of the province have faced more severe drought classes. In the second part of this research, hydrological drought monitoring was conducted in fifty third-order sub-basins located within the study province using the Total Water Storage (TWS) deficit, Drought Severity, and Total Storage Deficit Index (TSDI Index). Annual average calculations of the TWS deficit over the period from April 2012 to 2016 indicated a substantial depletion of groundwater reserves in the province, amounting to a cumulative loss of 12.2 km3 Analysis results indicate that drought severity continuously increased in all study basins until the end of the study period. Studies have shown that all the studied basins are facing severe and prolonged water scarcity. Among the 50 studied basins, the Rahmatabad basin, located in the semi-arid northern regions of the province, has experienced the most severe drought. This basin has experienced five drought events, particularly one lasting 89 consecutive months and causing a reduction of more than 665.99 km3. of water in month 1, placing it in a critical condition. On the other hand, the Niskoofan Chabahar basin, located in the tropical southern part of the province near the Sea of Oman, has experienced the lowest reduction in water volume with 10 drought events and a decrease of approximately 111.214 km3. in month 1. However, even this basin has not been spared from prolonged droughts. Analysis of drought index graphs across different severity classes confirmed that all watersheds experienced drought conditions, particularly in the later years of this period. Data analysis revealed a severe water crisis in the province. Urgent and coordinated actions are needed to address this challenge. Transitioning to drought-resistant crops, enhancing irrigation efficiency, and securing water rights are essential steps towards a sustainable future.

Drought, a complex natural hazard exacerbated by climate change, poses significant challenges to water resources, agriculture, livestock, and socio-economic stability globally. This study introduces a novel, multi-phase framework for regional drought monitoring and prediction. First, precipitation data quality is enhanced using auxiliary information, and the standardized drought indices (SDI) are computed through a 12-component Gaussian mixture distribution (12-CGMD), validated by Bayesian information criterion (BIC) values. Second, hierarchical clustering based on the Davies-Bouldin index (DBI = 1.7700) identifies six homogeneous clusters of meteorological stations, enabling the development of a multi-regional aggregated standardized drought index (MRASDI) through spatiotemporal bootstrapping. Distinct drought patterns were observed, including significant shifts from 1983 to 1996 in clusters 3, 4, and 5 and increased drought frequency in clusters 1 and 2 post- 1996. Lastly, the Boruta algorithm assesses meteorological station relevance within clusters, validating MRASDI. Machine learning models—random forest (RF) and support vector machines (SVM)—are applied to predict cluster-specific drought severity, with RF demonstrating superior accuracy, particularly in Cluster 5. Validation across 52 stations in Pakistan spanning 1968–2016 confirms the framework’s robustness. This study provides a scientifically robust tool for improving drought monitoring and prediction, offering insights for drought mitigation and climate resilience strategies tailored to regional needs.

The Global Precipitation Climatology Project (GPCP) Version 3.2 Precipitation Analysis provides globally complete analyses of surface precipitation on a 0.5° × 0.5° latitude–longitude grid at both monthly and daily time scales, covering from 1983 to the present and from June 2000 to the present, respectively. These merged products continue the GPCP heritage of incorporating precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, sounder-based estimates, and surface rain gauge observations emphasizing the strengths of various inputs and striving for time and space homogeneity. Furthermore, these analyses incorporate modern algorithms, refined intercalibrations among sensors, climatologies of recent high-quality satellite precipitation data, and fine-scale multisatellite estimates. New data fields have been introduced to better characterize the precipitation, including the fraction of the precipitation that is liquid (rain) in both the monthly and daily products, and a quality index for the monthly product. Compared to the operational GPCP Version 2.3 Monthly, the Version 3.2 Monthly product provides a more reasonable climatology in the Southern Ocean and increases the estimated global average precipitation by about 4.5%, which is similar to estimates from recent global water budget assessments. Global and regional trends for 1983–2020 with this new Monthly dataset are very similar to those computed from Version 2.3. Compared to the operational One-Degree Daily (Version 1.3) product, the new Version 3.2 Daily is designed to better represent the histogram of precipitation rates, particularly at high values and shifts the start of less-certain high-latitude estimates from 40° to 58° latitude in each hemisphere.