We employed R2D2, a multivariate bias-correction technique, to develop a dependence-preserved daily bias-corrected dataset of precipitation (P) and temperature (mean, maximum, and minimum) at 0.25° spatial resolution over India. The dataset spans the historical period (1951–2014) and future projections (2015–2100) under four Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, using outputs from 13 CMIP6 Global Climate Models (GCMs). The India Meteorological Department (IMD) gridded dataset at 0.25° resolution for precipitation and temperature (mean, maximum, and minimum) was used as the reference observational dataset for implementing the multivariate bias correction. The bias-corrected dataset was rigorously evaluated against observations using multivariate statistical metrics that capture dependence structures, including Pearson correlation, Kendall’s tau, the Clausius–Clapeyron (CC) relationship, and the representation of compound dry and hot extreme events involving precipitation and temperature (Tmax, Tmin, and Tmean). These evaluations ensure the reliability and quality of the bias-corrected dataset. The dataset has broad applicability for climate impact and future projection studies, particularly for research focused on precipitation–temperature dependence, compound extreme events, hydrological modelling, and drought assessment. The complete dataset is divided into four parts, with each part containing the bias-corrected data for an individual variable. Part-1 includes the bias-corrected precipitation dataset, provided in NetCDF (.nc) format. This part consists of five compressed files to reduce storage requirements: one historical dataset (Pr_historical.zip) and four future scenario datasets (Pr_SSP126.zip, Pr_SSP245.zip, Pr_SSP370.zip, and Pr_SSP585.zip). Each compressed file contains the bias-corrected NetCDF datasets from 13 CMIP6 GCMs, namely ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, EC-Earth3, EC-Earth3-Veg, INM-CM4-8, INM-CM5-0, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM, and NorESM2-MM.