Heatwaves Over Bangladesh: Long-Term Trends and Contributing Factors
Bibliographic record
Abstract
Heatwave intensity, duration, and frequency over Bangladesh have risen significantly in recent decades and are projected to increase further under continued global warming. Yet, their underlying characteristics and drivers remain underexplored. This study examines long-term trends and mechanisms of heatwaves across pre-monsoon and monsoon seasons during 1971–2024 using ERA5 reanalysis data. Results show that the monsoon is experiencing an accelerated warming trend, with daily maximum temperature (T2Mmax) increasing at a rate three times greater than that of the pre-monsoon. Simultaneously, daily maximum equivalent potential temperature (EPTmax) is rising nine times and over three times faster than the corresponding T2Mmax rates in the pre-monsoon and monsoon, respectively. These changes have caused the frequency of extreme T2Mmax and EPTmax to increase by 30% and 167% in the pre-monsoon and by 457% and 661% during the monsoon, respectively. Consequently, the number of mean heatwave days has increased by about 25 days during the pre-monsoon and 40 days during the monsoon since 2000. Synoptic analyses reveal that quasi-stationary mid-tropospheric anticyclones are the primary drivers of heatwaves, which promote subsidence, cloud suppression, and enhanced surface radiative heating. Land-atmosphere interactions then further exacerbate these conditions. Collectively, these findings demonstrate that circulation patterns, soil moisture, and humidity play critical roles in driving the intensity and persistence of heatwaves over Bangladesh. This study advances the understanding of regional heatwave dynamics and provides actionable insights for evidence-based climate adaptation, disaster risk reduction, and public health resilience planning in South Asia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".