Characteristics of precipitation systems in and around Bangladesh are analysed using 6 years of radar data from the Bangladesh Meteorological Department (BMD). Precipitation systems are classified as arc, line, or scattered types according to their shape; then as small, medium, or large according to their length; and as stationary, slow, or fast according to their propagation speed. Arc-, line-, and scattered-type systems are found 230 (29%), 117 (15%), and 442 (56%) times, respectively, from April to September during the analysis period (2000)(2001)(2002)(2003)(2004)(2005). These arc-, line-, and scattered-type systems have average speeds of 11.0, 7.1, and 5.8 m/s, horizontal lengths of 185, 184, and 268 km, and approximate lifetimes of 4.3, 4.0, and 4.8 h, respectively. Scattered-type systems dominate in the monsoon period (June, July, August, and September), while arc-type systems dominate in the pre-monsoon period (April and May). Line-type systems occur with nearly equal frequency in both periods. The monsoon systems are large and stationary or slow moving. In contrast, pre-monsoon systems are small and fast moving. Pre-monsoon systems propagate southeastward whereas monsoon systems propagate to the northeast, northwest, or southeast. A large number of systems occur in the southern, eastern, and northern parts of Bangladesh. Of the 442 scattered-type systems, 244 are scattered-type systems having wide areal coverage (SWAC), which extends out of radar range, and speeds too small to calculate. About 97% of SWACs develop during the monsoon period and contribute greatly to monsoon rainfall in this region.

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Satellite‐derived lightning data for 17 years (1998–2014) were used to evaluate the relation between environmental factors and lightning activity over the Bangladesh landmass. Time series convective available potential energy (CAPE) data were extracted from ERA‐40 reanalysis data while total and convective rainfalls were obtained from Tropical Rainfall Measuring Mission's monthly products. In addition, the product of CAPE and precipitation was computed and used as an additional variable. Three timescales – monthly, seasonal and annual – were utilized to determine the influence of precipitation and CAPE on lightning activity. The results indicated that CAPE stands out as an important variable at all of these timescales for predicting the occurrence of lightning. The correlation coefficient (r) between CAPE and lightning activity was found to be 0.902 (monthly), 0.703 (pre‐monsoon), 0.550 (monsoon) and 0.702 (annual), respectively. Total rain showed strong positive correlation with lightning on monthly scale (r = 0.734) and in the pre‐monsoon season (r = 0.701). However, such relationship was moderate during monsoon (r = 0.455). In contrast, convective rain showed slightly higher correlation during monsoon (r = 0.587) compared with that of pre‐monsoon season (r = 0.532). Because of strong seasonality in the data, convective rain did not exhibit strong relationship on annual scale (r = 0.227). The product variable (e.g. CAPE × precipitation) showed significant correlation on monthly (r = 0.895) and seasonal scales (r = 0.818 during pre‐monsoon and 0.686 in monsoon) but its influence appears to diminish on a longer timescale (r = 0.375). Spatial maps of correlation coefficient revealed significant positive correlation along relatively drier northern parts of Bangladesh. As lightning‐related fatality is on the rise, this study, first of its kind, is expected to inform public policy and provide information necessary for effective management of this atmospheric phenomenon to save lives and property in Bangladesh.

Although coastal and inland areas of Bangladesh exhibit distinct physiographic and climatic characteristics, spatiotemporal variation of extreme climatic events is poorly understood in these two areas. This study was an attempt to understand the trends in extreme climatic events in coastal and inland areas over the period 1968–2018. The missing data in daily maximum and minimum temperature, and daily rainfall datasets were imputed using the multiple imputation by chained equations technique and implementing a predictive mean matching algorithm. The imputed datasets were then tested for inhomogeneity using the penalized maximal t and modified penalized maximal F tests. A quantile matching algorithm was then applied to homogenize the datasets, which were then used for generating 13 extreme temperature and 9 extreme rainfall indices. The trends were assessed using the Trend Free Pre‐whitened Mann–Kendall test and the magnitudes of the changes were determined using the Thiel–Sen slope estimator. Additionally, standardized anomalies were calculated to understand the seasonal variability of temperature and rainfall over the past five decades. Results suggested that both coastal and inland areas were warming significantly but coastal areas exhibited a higher rate of warming. Although most of the extreme rainfall indices showed statistically non‐significant changes for coastal and inland stations, there is evidence of localized dryness and increased rainfall at individual stations. In particular, the drought‐prone northwestern region of the country experienced decreased rainfall, which is discordant to the results of previous studies. Findings from this study highlighted important local and regional‐scale changes in extreme climate events that were previously overlooked. The findings of this study can help undertake targeted climate change adaptation strategies to save population and resources.

Providing Regional Climates for Impacts Studies (PRECIS) is a regional climate model, which is used for the simulation of regional-scale climatology at high resolution (i.e. 50-km horizontal resolution). The calibration of rainfall and temperature simulated by PRECIS is performed in Bangladesh with the surface observational data from the Bangladesh Meteorological Department (BMD) for the period . The Climate Research Unit (CRU) data is also used for understanding the performance of the model. The results for the period 1961-1990 are used as a reference to find the variation of PRECIS-projected rainfall and temperature in 2071, in and around Bangladesh, as an example. Analyses are performed using the following two methods: (1) grid-to-grid and (2) point-to-point analyses. It is found that grid-to-grid analysis provides overestimation of PRECIS in Bangladesh because of downscaling of observed data when gridded from asymmetric low-density data network of BMD. On the other hand, model data extracted at observational sites provide better performance of PRECIS. The model overestimates rainfall in dry and pre-monsoon periods, whereas it underestimates it in the monsoon period. Overall, PRECIS is found to be able to estimate about 92% of surface rainfall. Model performance in estimating rainfall increases substantially with the increase in the length of time series of datasets. Systematic cold bias is found in simulating the annual scale of the surface temperature. In the annual scale, the model underestimates temperature of about 0.61°C that varies within a range of +1.45°C to −3.89°C in different months. This analysis reveals that rainfall and temperature will be increased in Bangladesh in 2071. On the basis of the analyses, look-up tables for rainfall and temperature were prepared in a bid to calibrate PRECIS simulation results for Bangladesh. The look-up tables proposed in this analysis can be employed in the application of the projected rainfall and temperature in different sectors of the country. These look-up tables are useful only for the calibration of PRECIS simulation results for future climate projection for Bangladesh.

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To explore the teleconnections between Bangladesh summer monsoon rainfall (BSMR) and sea surface temperature (SST) anomalies over different parts of the ocean have been examined from the years 1961-2008 (48-year). Global NCEP/NCAR reanalysis data of SST have been used at 2 degree by 2 degree grid in this study. A significant positive correlation (0.44) was found between the BSMR and SST over the southwest Indian Ocean (around 30 o S) in the month of February. All other monthly (January to May) correlations were not found to be significant. The SST over the Bay of Bengal was positively correlated with BSMR but not significant. During summer monsoon season the prevailing wind has a south-westerly direction over Bangladesh. The SST over the southwest Indian Ocean is especially important at 30 o -36 o S latitude and 74 o -78 o E longitude to develop the linear regression model to predict summer monsoon rainfall in Bangladesh.