Investigation of Changes in Extreme Temperature and Humidity Over China Through a Dynamical Downscaling Approach
Bibliographic record
Abstract
Abstract Impacts of climate change relating to public health are often determined by multiple climate variables. The health‐related metrics combining high‐temperature and relative humidity are most concerned. Temperatures, relative humidity and relationship among them are investigated here for a comprehensive assessment of climate change impacts over China. A projection of combined temperatures and humidity through the PRECIS model is addressed. The PRECIS model's skill in reproducing the historical climate over China was first gauged through validating its historical simulation with the observation data set in terms of the two contributing variables. With good results of validation, a plausible range of combined temperatures and relative humidity were generated under RCPs. The results suggested that the annual mean temperature of China will increase up to 6°C at the end of 21st century. Opposite to the significantly change in the temperature, the maximum magnitude of changes in relative humidity is only 8% from the value in the baseline period. The dew point temperature is projected to be 14.9°C (within the comfortable interval) over the whole nation under high radiative forcing scenario at the end of this century. Therefore, the combination effects of high temperatures and relative humidity are substantially smaller than generally anticipated for China. Even though the impact‐relevant metric like the dew point temperature is not projected as bad as the generally anticipated, we found that the frequency of high‐temperature extremes increases up to 40% and the duration increases up to 150% in China. China is still expected to have more number of extremely hot days, more frequent high‐temperature extremes, and longer duration of warm spell than before. Regionally, South China has the smallest changes in the mean, maximum and minimum temperatures while the largest increases in all five high‐temperature indices. Consequently, the climate over South China for two future periods will be changing more drastically than the baseline period. Extra cautions need to be given to South China in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".