Non-stationary Multivariate Bias-corrected CMIP6 Climate Projections of Daily Precipitation and Temperature over India [R Scripts]

General circulation models (GCMs) exhibit systematic biases and coarse spatial resolution that limit their direct applicability for regional climate analyses, particularly under non-stationary climate conditions. While statistical bias correction (BC) methods are widely applied, most adjust each variable independently and therefore do not explicitly preserve cross-variable dependence structures required for physically consistent multivariate climate assessments. A non-stationary multivariate bias-corrected daily dataset of precipitation and maximum, minimum, and mean temperature for India at 0.25° spatial resolution is presented, derived from 13 CMIP6 GCMs using the Rank Resampling for Distributions and Dependences (R2D2) framework. The approach accounts for evolving distributional characteristics under changing climate conditions while preserving inter-variable dependence. The dataset spans the historical period (1951–2014) and future projections (2015–2100) under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Performance is evaluated against observations using multivariate statistical properties that capture dependence structures, including Pearson correlation, Kendall correlation, Clausius–Clapeyron scaling and copula-based dependence modelling between precipitation and temperature. The resulting dataset provides a physically coherent resource for regional climate impact assessments, compound event analysis, hydrological modelling, and drought studies under non-stationary climate conditions. The R scripts implementing the R2D2 multivariate bias correction framework are available. The repository includes two scripts: Historical_R2D2.R for bias correction of historical CMIP6 simulations and Future_R2D2.R for bias correction of future CMIP6 projections.