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Assessing anthropogenic climate change in a regional context is challenging due to the spatial heterogeneity of climatic variables and is more complicated than at the global scale. Especially in the Tropics, such spatial variations are expected to increase, warranting the identification of homogeneous climatic zones for assessing regional climate change. The present study explores the ability of bioclimatic variables in defining regional climatic zones, and the detection of climate change therein. We hypothesize that the identification of homogeneous climatic zones based on bioclimatic variables could be an effective approach rather than the conventional extreme climate‐based indices to identify climate change signals. To demonstrate the hypothesis, bioclimatic variables representing the generalized climatic characteristics of a tropical river basin were derived from observed gridded datasets of rainfall and temperature. Clusters of homogeneous climatic zones were identified, and their temporal variations were analysed to examine the existence of climate change. The results indicate that despite the spatial heterogeneity in extreme climate‐based indices, the bioclimatic variables‐based approach renders a meaningful representation of the regional climatic pattern. Investigation of bioclimatic zones of the study area helped to identify a shift in its climatic zones with a slant towards drier conditions. Further, future changes in climatic zones were identified from 13 different GCMs that participated in the CMIP6, projecting drier conditions over the basin, with varying spatial extend based on future emission scenarios. The study significantly contributes towards the identification of climatologically fragile regions in changing climate, which is an essential component in developing any regional climate change adaptation and mitigation strategy. [ABSTRACT FROM AUTHOR]

Monsoon climate over India has high degree of spatio-temporal heterogeneity characterized by the existence of multi-climatic zones along with strong intra-seasonal, seasonal, and inter-annual variability. Vegetation growth of Indian forests relates to this climate variability, though the dependence structure over space and time is yet to be explored. Here, we present a comprehensive analysis of this association with quality-controlled satellite-based remote sensing dataset of vegetation greenness and radiation along with station based gridded precipitation datasets. A spatio-temporal time-frequency analysis using wavelets is performed to understand the relative association of vegetation growth with precipitation and radiation at different time scales. The inter-annual variation of forest greenness over the Tropical India are observed to be correlated with the seasonal monsoon precipitation. However, at inter and intra-seasonal scales, vegetation has a strong association with radiation in regions of high precipitation like the Western Ghats, Eastern Himalayas, and Northeast hills. Forests in Western Himalayas were found to be correlated more on the winter precipitation from western disturbances than the south west monsoon precipitation. Our results provide new and useful region-specific information for dynamic vegetation modelling in the Indian monsoon region that may further be used in understanding global vegetation-land-atmosphere interactions. [ABSTRACT FROM AUTHOR]