A Comparative Study of Spatial Interpolation Methods for CMIP6 Monthly Historical and Future Hydro-climatic Datasets for Indian Region

The Global Climate Models (GCMs) provides one of the most credible datasets to understand the past and future of earth’s climate. Regional Climate Models (RCMs) are applied to obtain climate information from GCMs to regional level. In practice, spatial interpolations are applied to re-grid coarser resolution GCM data to finer resolution before it is fed to RCM. Standard interpolation algorithms are proposed in literature, however mostly they are selected arbitrarily, especially for interpolation of GCM data. This study is envisaged to provide a comparative assessment of six interpolation methods for spatial interpolation of Sixth phase Coupled Model Intercomparison Project (CMIP6) datasets from six different GCMs. No existing study provides their comparative performance for CMIP6 variables specific to the Indian region. The performance evaluation of selected methods is carried out for seven important hydrological parameters at monthly time scale. The selected parameters are interpolated to 0.25 degree spatial resolution for the entire Indian region. Based on skill tests performed, the Bilinear and Bicubic interpolation methods are realized to be suitable choices for gridded data interpolation.