IPEDClim: a high-resolution dataset of bioclimatic variables for ecohydrological applications in India

Abstract

High-resolution gridded climate datasets are essential for ecological, agricultural, and environmental research. Species distribution models, which link species-occurrence records with environmental predictors, critically depend on bioclimatic variables (BCVs). In India, most studies rely on global BCVs such as WorldClim, CHELSA-bioclim and MERRAclim, which do not adequately represent local climate heterogeneity or integrate the national weather station network. To address this gap, we present IPEDClim, a 0.10∘ ( ∼10 km) resolution dataset comprising of 19 precipitation and temperature-based BCVs derived from the observation-based Indian Precipitation Ensemble Dataset (IPED) and the EM-Earth. The dataset includes precipitation variables developed from 1991 to 2023, while temperature and combined variables were developed from 1991 to 2019. The variables capture key climatic attributes, including mean annual temperature, diurnal temperature range, seasonality, and precipitation extremes. The ensemble-mean construction enhances reliability in a region with a sparse observational network and improves fidelity relative to existing global products. In addition, we conducted a grid-based trend assessment (1991–2019/2023) using the non-parametric Mann–Kendall test and Sen’s slope at 0.10∘ resolution to quantify monotonic changes in each BCV. The results showed widespread warming across most of India, with annual mean temperature (BIO1) recording positive Sen’s slope values (+0.02 to +0.04∘C yr −1). Precipitation variables showed marked regional variability, including declining wettest-month precipitation (BIO13) of 10–20 mm yr −1 across the Western Ghats, central India and Northeast India, localized increases in driest-month precipitation (BIO14), and declining cold-season precipitation (BIO19) of 12.5–25 mm yr −1 across the Himalayan region and Northeast India. The dataset provides a robust foundation for ecological modeling, biodiversity conservation, agricultural planning, and climate impact assessments in India. Through the integration of high-resolution, observation-based ensembles, IPEDClim enables more detailed climate characterization and supports evidence-based decision-making in ecosystem management, water resource planning, and disaster risk reduction.