Mohammad Arab Amiri

Drought regionalisation remains sensitive to index selection, temporal scale and clustering methodology, especially in hydroclimatically transitional regions. This study introduces a multi‐index multi‐scale meteorological drought regionalisation framework based on long‐term historical precipitation. The new approach starts with calculations of two standardised meteorological drought indices, Standardized Precipitation Index (SPI) and China‐Z Index (CZI), at 1‐, 3‐ and 12‐month time scales. Then, Principal Component Analysis (PCA) is applied for dimensionality reduction, followed by K‐means clustering to delineate homogeneous drought regions. To ensure methodological robustness, cluster validation is performed using Variance Ratio Criterion (VRC). The proposed approach was demonstrated using monthly precipitation data from 28 climatological stations (1946–2023) distributed across Serbia. The results indicated the presence of three statistically stable drought regions largely governed by topography and continentality gradients. Mountainous western and southern Serbia exhibit high cross‐scale stability (> 80% station persistence), whereas lowland northern regions show moderate sensitivity to index selection. SPI‐based regionalisation demonstrates stronger interannual variability representation, while CZI produces smoother regional drought signatures due to distributional assumptions. Crni Vrh and Zlatibor climatological stations in the mountainous regions of southwestern and central Serbia are representative stations with scale‐resistant drought characteristics. The most detailed spatial resolution that identified six distinct homogeneous sub‐zones is produced by the proposed approach across all time scales. The VRC across all index‐scale combinations is used to validate the stability of the identified regions. The proposed stability‐enhanced framework improves the reliability of drought regionalisation and provides a transferable methodology for climate transition zones under non‐stationary hydroclimatic conditions.

The challenges and prospects for Aerosol-Cloud-Precipitation Studies have been discussed in the manuscript. I think this is a short discussion paper and not an original work. I have listed my major concerns below: The novelty of the manuscript has n...

Spatial and temporal variability analysis of precipitation is an important task in water resources planning and management. This study aims to analyze the spatial and temporal variability of precipitation in the northeastern corner of Iran using data from 24 well-distributed weather stations between 1991 and 2015. The mean annual rainfall, precipitation concentration index (PCI), and their coefficients of variation were mapped to examine the spatial variability of rainfall. An artificial neural network (ANN) in association with the inverse distance weighted (IDW) method was proposed as a hybrid interpolation method to map the spatial distribution of the detected trends of mean annual rainfall and PCI over the study region. In addition, principal component analysis (PCA) was applied to annual precipitation time series in order to verify the results of the analysis using the mean annual rainfall and PCI data sets. Results show high variation in inter-annual precipitation in the west, and a moderate to high intra-annual variability over the whole region. Irregular year-to-year precipitation concentration is also observed in the northeastern and northwestern parts. All in all, the highest variations in inter-annual and intra-annual precipitation occurred over the western and northern parts, while the lowest variability was observed in the eastern part (i.e., the coastal region).