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Bangladesh is known as one of the countries that are most vulnerable to climate change, which promotes continuous scientific attention to the changes in the regional climate of this country. This study examines the long-term changes in the climate of Bangladesh using reanalysis data during the period of 1959–2021, utilizing the linear regression method. Bangladesh has experienced top-heavy tropospheric warming, with the temperature increasing at a rate of 0.21 °C decade−1 at 300 hPa and 0.07 °C decade−1 at 850 hPa, which has led to a (statistically) significant increase in the tropospheric static stability. The increase in tropospheric stability is most pronounced in the pre-monsoon season, in which the lower tropospheric warming has not occurred. In contrast, the post-monsoon and winter seasons have experienced prominent lower tropospheric warming. In conjunction with the tropospheric warming, the troposphere over Bangladesh has also undergone moistening, indicated by a 6% increase in precipitable water during the study period. The tropospheric moistening is most prominent in the monsoon season. This study reveals that the two rainiest seasons have experienced different long-term changes in precipitation characteristics. In the pre-monsoon season, the precipitation intensity has significantly decreased by 9% during the study period, which is attributable to the stabilization of the upper troposphere and consequent decrease in the potential of deep convection. In the monsoon season, the precipitation amount has significantly decreased by 10% during the study period. This decrease has occurred exclusively in the eastern part of Bangladesh, and it is primarily attributed to the weakening of monsoonal southerly flow and consequent decrease in moisture flux convergence there. [ABSTRACT FROM AUTHOR]

Satellite retrieval-based precipitation data with high spatial and temporal resolutions give an opportunity for precipitation research in regions with coarse networks of precipitation measuring instruments. This study evaluates the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) data in Bangladesh and surrounding regions and then examines the diurnal variation of precipitation in these regions using the cyclostationary empirical orthogonal function (CSEOF) analysis method. The IMERG data capture the overall patterns of the diurnal variation of precipitation well, except for the overestimation of the degree of diurnal variability in the pre-monsoon season. The spatial distribution of precipitation is also captured well, except for the drastic horizontal change in precipitation within the northeastern region of Bangladesh. These encourage the use of the IMERG data for studying precipitation characteristics in Bangladesh and surrounding regions. Through the CSEOF analysis, the pre-monsoonal precipitation is characterized by the enhanced precipitation in the northern region of Bangladesh and the Meghalaya Plateau region in the late night to early morning. The monsoonal precipitation consists of two contrasting CSEOF modes. One shows enhanced precipitation in the northern region of the Bay of Bengal during 0600–1500 LST, related to the strong westerly moisture transport over the Bay of Bengal. The other shows enhanced precipitation in the southern slopes of the Meghalaya Plateau and Himalayan Foothills during 0000–0600 LST, associated with the strong southwesterly moisture transport toward these slopes that is neither blocked nor deflected by the Arakan Mountains. [ABSTRACT FROM AUTHOR]

The accretion process in bulk cloud microphysics schemes can be parametrized using the stochastic collection equation (SCE). In this study, the collection efficiency of each raindrop–cloud droplet pair is applied to the SCE to derive a new accretion parametrization that considers a strong variability of accretion rate depending on the cloud droplet and raindrop size distributions. To evaluate the new accretion parametrization (NP), it is implemented into a cloud‐resolving model, replacing the original accretion parametrization (OP) based on the continuous collection equation. In the idealized simulations of deep convective clouds, NP predicts overall larger accretion rates and smaller autoconversion rates than OP. The resultant high accretion/autoconversion rate ratio in NP increases the mean raindrop size. This induces faster sedimentation of raindrops that is associated with the earlier onset of surface precipitation and also weakens the evaporation cooling of raindrops that can affect the thermodynamics and dynamics of clouds. Meanwhile, for a given pair of rainwater and cloud water mass contents, the accretion rates in NP have a broad distribution while those in OP are less variant, suggesting that the dependence of the accretion rates on bulk microphysical properties other than the mass contents also needs to be properly considered in accretion parametrizations. Most of the aforementioned differences between the two accretion parametrizations found in the idealized simulations are also found in the real‐case simulations of a precipitation event over Bangladesh but the differences are smaller, and the spatial distribution of the accumulated precipitation amount is relatively well predicted by NP compared to OP. [ABSTRACT FROM AUTHOR]