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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

To address the limitation where the traditional Optimal Threshold Selection (OTS) scheme achieves a high Threat Score (TS) at the expense of an increased False Alarm Rate (FAR), this study develops an Objective Improvement of Optimal Threshold Selection (OIOTS) scheme based on the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble precipitation forecasts. The correction performance is verified using high-resolution observations during the post-flood season in Guangdong Province. The results indicate that (1) optimal quantiles exhibit significant spatial heterogeneity and decrease sharply with increasing precipitation intensity, confirming the necessity of grid-specific correction over uniform provincial thresholds. (2) The Optimal Precipitation (OP) threshold remains stable across different lead times but shows distinct regional characteristics influenced by topography, whereas the corresponding Probability Threshold (PT) demonstrates a downward trend as the lead time extends. (3) Verification reveals that, compared with the OTS scheme, the OIOTS scheme effectively rectifies the high FAR inherent in the optimal quantile method while maintaining a comparable TS. By minimizing the absolute difference between TS and FAR, the OIOTS scheme achieves a superior balance between detection accuracy and error suppression, with its FAR showing a significant downward trend as precipitation magnitude and lead time increase. Given its high computational efficiency and robust performance, the proposed scheme offers a reliable solution for operational meteorological forecasting.

Improvements in skill of summer forecasted precipitation as produced by the North American multi‐model ensemble (NMME) system over the contiguous United States (CONUS) are examined by applying a new bias correction method. The uncorrected precipitation produced by NMME hindcasts exhibits good prediction skill in fall and winter, while the spring and summer forecasts are marked with extremely poor skill. We propose a new method to correct the forecasted precipitation distribution based on skillfully predicted 2‐m air temperature (T2m) forecasts to fully exploit the stronger co‐variability that exists between precipitation and T2m in nature. The occurrence of enhanced recycled precipitation over CONUS provides an ideal situation to hone precipitation forecast skills using the T2m forecasts. The proposed bias correction is shown to successfully reduce the root mean square error in precipitation hindcasts in summer and can easily be extended to real‐time forecasts, thus providing a framework to dynamically link precipitation with other predictors besides T2m. Process understanding of the observed T2m‐precipitation relation will offer a framework for diagnosing poor model skill. Improvements in skill of summer forecasted precipitation as produced by the North American multi‐model ensemble (NMME) system over the contiguous United States (CONUS) are examined by applying a new bias correction method. The uncorrected precipitation produced by NMME hindcasts exhibits good prediction skill in fall and winter, while the spring and summer forecasts are marked with extremely poor skill. We propose a new method to correct the forecasted precipitation distribution based on skillfully predicted 2‐m air temperature (T2m) forecasts to fully exploit the stronger co‐variability that exists between precipitation and T2m in nature. The occurrence of enhanced recycled precipitation over CONUS provides an ideal situation to hone precipitation forecast skills using the T2m forecasts. The proposed bias correction is shown to successfully reduce the root mean square error in precipitation hindcasts in summer and can easily be extended to real‐time forecasts, thus providing a framework to dynamically link precipitation with other predictors besides T2m. Process understanding of the observed T2m‐precipitation relation will offer a framework for diagnosing poor model skill.

The output from Global Forecasting System (GFS) T574L64 operational at India Meteorological Department (IMD), New Delhi is used for obtaining location specific quantitative forecast of maximum and minimum temperatures over India in the medium range time scale. In this study, a statistical bias correction algorithm has been introduced to reduce the systematic bias in the 24–120 hour GFS model location specific forecast of maximum and minimum temperatures for 98 selected synoptic stations, representing different geographical regions of India. The statistical bias correction algorithm used for minimizing the bias of the next forecast is Decaying Weighted Mean (DWM), as it is suitable for small samples. The main objective of this study is to evaluate the skill of Direct Model Output (DMO) and Bias Corrected (BC) GFS for location specific forecast of maximum and minimum temperatures over India. The performance skill of 24–120 hour DMO and BC forecast of GFS model is evaluated for all the 98 synoptic stations during summer (May-August 2012) and winter (November 2012–February 2013) seasons using different statistical evaluation skill measures. The magnitude of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for BC GFS forecast is lower than DMO during both summer and winter seasons. The BC GFS forecasts have higher skill score as compared to GFS DMO over most of the stations in all day-1 to day-5 forecasts during both summer and winter seasons. It is concluded from the study that the skill of GFS statistical BC forecast improves over the GFS DMO remarkably and hence can be used as an operational weather forecasting system for location specific forecast over India.