Inter-annual variability of pre-monsoon surface air temperatures over India using the North American Multi-Model Ensemble models during the global warming era

In the present study, the efficacy of the North American Multi-Model Ensemble (NMME) seasonal prediction models initialized by February month in simulating the climatological features and inter-annual variability of Summer (March through May; MAM) Surface Air Temperature (SSAT) over India is validated with the observations during 1981–2018. Among the six models, the CanCM4i model can capture the observed increasing trend of maximum, minimum, and mean temperatures over the Indian landmass with slight variation (trend values: 0.84 °C/38 years, 1.1 °C/38 years, and 0.96 °C/38 years). But, statistical analysis reveals that the CanCM4i model has more clod bias than the rest of the models (with less mean bias and RMSE) and in good agreement (slightly) with the observations. Most of the NMME models unable to simulating the extremely warm and cold events over the Indian region. Around 20–30% of extreme events are captured well like in the observations than in the normal years (high threat score). Furthermore, the dominant leading modes (by EOF1 and EOF2) of climate variability over India indicate that all NMME models underestimate (overestimate) the total variance of the first (second) dominant mode of SSAT than the observation except the Gem-NEMO model. Gem-NEMO model has a better total variance for EOF1 (63.84%) and EOF2 (11.23%) than other models. The IC3 (IC3-CanCM4, IC3-CanSIPS, IC3-Gem5-NEMO) models reasonably captured the association between principal component-1 and sea surface temperature (SST) anomalies over the eastern Pacific and positively over the south Atlantic region as in the observations. The NMME models have shown the SST anomalies over the eastern Pacific, Indian, and Atlantic Oceans, which are against to the observations except for the Gem-NEMO model. Thus, there is a large diversity in the representation of climatology features, inter-annual variability, and two dominant modes of variability of SSAT over India in present generation coupled seasonal prediction models.