Optimization of CMIP6 models for simulation of summer monsoon rainfall over India by analysis of variance.

The advent of weather and climate models has equipped us to forecast or project monsoon rainfall patterns over various spatiotemporal scales; however, utilizing a single model is not usually sufficient to yield accurate projection due to the inherent uncertainties associated with the individual models. An ensemble of models or model runs is often used for better projections as a multimodel ensemble (MME). This study analyzes the accuracy of MME in simulating the Indian summer monsoon rainfall (ISMR) variability using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The results highlighted that although the MME primarily reproduces the observed pattern and annual cycle of rainfall, significant biases are noted over homogeneous meteorological regions of India, except northeast India. To overcome this issue, an analysis of variance (ANOVA) and post hoc statistical tests are employed to identify a group of models for which the modified MME gives a better estimate of rainfall and reduces the bias significantly. Our findings underscore the potential of ANOVA and post hoc tests as a practical approach to enhancing the accuracy of multimodel ensemble rainfall for the assessment of model projections. [ABSTRACT FROM AUTHOR]