Global spatiotemporal variation analysis and AI prediction of terrestrial high-temperature droughts
Bibliographic record
Abstract
In recent years, the frequency and intensity of compound high-temperature drought events have significantly increased on a global scale, posing severe challenges to agricultural production and ecological environments. To elucidate the spatiotemporal variation patterns of such extreme events and enhance prediction accuracy, this study systematically analyzed the distribution patterns and evolutionary trends of terrestrial high-temperature drought events based on the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI), utilizing global multi-source observational data from 1980 to 2022. The results indicate that regions such as Brazil, West Africa, the Arabian Desert, South Asia, and Mexico exhibit particularly prominent high-temperature trends, while precipitation significantly decreases in parts of South America, South Asia, Libya, western United States, eastern Canada, and southwestern China. Additionally, the recurrence intervals of high-temperature droughts in Venezuela, Brazil, northern Russia, Iran, and southwestern China have markedly shortened. To further improve prediction accuracy, this study employed wavelet transforms in combination with three deep learning methods—Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory Network (LSTM)—to develop multi-scale predictive models for SPI and STI. The results demonstrate that all three models achieved coefficients of determination (R 2 ) exceeding 0.98 for SPI and STI predictions, with mean absolute errors (MAE) below 0.036 and 0.07, root mean squared errors (RMSE) below 0.09 and 0.05, respectively, indicating high reliability in extreme event prediction. Forecasts for 2019–2026 suggest that the frequency and intensity of compound high-temperature drought events will generally continue to rise, providing critical references for subsequent climate risk assessments and agricultural disaster prevention and control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".