Heatwave (HW) events are expected to worsen due to climate change, resulting in increased societal impacts such as human illness, crop failures, wildfires, power outages, infrastructure disruption, and damage. The southwest region of Bangladesh, particularly the Khulna division, is anticipated to experience severe consequences of climate change, with increased frequency and intensity of HWs causing numerous deaths due to heatstroke. To understand and mitigate the severity of HWs in Khulna, various aspects were analyzed, such as frequency, duration, and magnitude. This involved calculating the yearly number of HW events (HWN), the yearly sum of HW days (HWF), the length of the longest yearly event (HWD), the average size of all yearly events (HWL), the hottest day of the hottest yearly event (HWA), and the average magnitude of all yearly events (HWM). This analysis used daily maximum temperature records from four observation stations during the summer months (March to May or MAM) from 1990 to 2014, as well as data from thirteen CMIP6 models for three time periods: historical (1990-2014), near future (2026-2050), and far future (2076-2100) for two emission scenarios SSP2-4.5 and SSP3-7.0. Observation data were used to correct the bias of CMIP6 models using empirical quantile mapping (EQM) and to evaluate these models to characterize HW characteristics. HW events were identified using the 90th and 75th percentiles of the distribution of daily maximum temperature values for the reference period. Based on the Pearson Correlation Coefficient (PCC), the values of indices from GCMs are not strongly correlated with the values from the observed dataset. Additionally, indices like HWA and HWM are calculated from GCMs, which are significantly less correlated with the results from the observed dataset, that denotes GCMs cannot project extreme values for particular HW events . The frequency, duration, and magnitude of HWs showed abrupt changes for both emission scenarios compared to the historical period. The historical pattern of HW indices of the Khulna Division has not changed much over 25 years except HWF. HWs are proving to be the deadliest disaster in modern existence. Therefore, a comprehensive understanding of the characteristics of HWs in a particular area is crucial for raising awareness and potentially saving many lives.