Standardized drought indices such as the Standardized Precipitation Index (SPI) are widely used in both monitoring and climate-change impact assessments. However, SPI values are not uniquely defined unless the reference climatology used for standardization is explicitly stated and justified−a methodological issue that becomes critical under non-stationary climate conditions. Here, we present a methodological assessment of how reference-climatology strategy affects SPI-based drought projections under climate change, using Türkiye’s 26 major basins as a hydroclimatically diverse testbed. These assessments inform sustainable water resources planning, agricultural adaptation, and climate-resilient infrastructure design under non-stationary climate. Daily precipitation projections from 56 GCM-RCM pairs (EURO-CORDEX EUR-11, 0.11° (approximately 12 km at the mid-latitudes of the study domain); CMIP5 RCP8.5) were bias-corrected against ERA5-Land and aggregated to basin means. We computed SPI-9 and compared two commonly used reference strategies: (i) a fixed historical baseline (1970–2005), applied consistently to both historical and future periods (fixed-baseline SPI); and (ii) a period-specific baseline (period-specific SPI; future SPI values are standardized to the climatology of the future evaluation period itself). Using the same climate simulations, the two strategies yield markedly different drought projections. At the country scale, end-of-century drought time reaches 458 months under the fixed-baseline strategy, whereas the period-specific strategy indicates 393 drought months. Corresponding severity summaries are likewise stronger under fixed-baseline standardization. The contrast is even stronger in several Mediterranean basins, where fixed-baseline standardization produces persistently severe drought conditions. These results show that SPI-based drought projections are substantially sensitive to the choice of reference-climatology strategy, and that the same climate ensemble can support materially different drought narratives depending on how anomalies are standardized. Because the two strategies differ in both reference-timing and calibration-window length (36 versus 95 years), the headline contrast should be interpreted as a combined effect rather than as a pure baseline-timing result. In the present implementation, the period-specific strategy uses a single future calibration period (2006–2100), so the comparison should be interpreted as a stress test of reference framing under non-stationary climate rather than as an equal-length baseline experiment. An equal-length late-baseline sensitivity check (1970–2005 versus 2065–2100; both spanning 36 years) shows that the fixed-to-late-baseline contrast is larger than the fixed-to-period-specific contrast in 25 of 27 spatial units, including a 3.0-fold amplification at the national scale, indicating that the reference-timing effect persists when calibration-window length is held constant. Because the analysis is based on a CMIP5-driven RCP8.5 ensemble, the numerical projections should be interpreted as a high-end stress-test envelope rather than as the most likely outcome. We therefore recommend that drought projection studies explicitly report the reference-climatology strategy, justify the calibration window, and distinguish between analyses designed to quantify change relative to a historical climate and analyses designed to describe anomalies relative to an evolving future climate. These methodological choices have direct implications for sustainable water resources management and drought-risk preparedness in water-stressed Mediterranean systems, and contribute to broader sustainability targets such as Sustainable Development Goal 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land).

Climate change is shifting building thermal demand from heating toward cooling, yet quantitative, design-relevant projections for semi-arid continental cities remain scarce. This study projects heating and cooling degree days (HDDs and CDDs) for Ankara, Türkiye, using ERA5-Land as the reference, and a quantile delta mapping (QDM) bias-corrected CMIP6 ensemble under SSP2-4.5 and SSP5-8.5, across eight base temperatures (HDD 15–18 °C; CDD 23–26 °C) to 2100. By the far future, HDD18 decreases by 23–37% whereas CDD24 increases by +205 to +498 °C·day/year (with wide inter-model spread), and the heating degree days factor declines from 0.984 to 0.765, marking a shift toward cooling within the degree-day budget; the cooling season extends from 25 to as many as 106 days. In a deterministic −1 to +2 °C local-temperature-offset test, the direction of change persists, and the cooling design dry-bulb temperature rises by up to +7.3 °C, raising peak cooling loads. Independent 2020–2022 station observations support the HDD projections (CDD projections were not independently validated). Because a single grid cell and station cannot resolve urban canyon or local climate zone contrasts, the conclusions apply to the campus vicinity. Overall, the study translates the climate signal into HVAC-sizing and retrofit-relevant metrics for a warming semi-arid capital.

Accurate deterministic forecasts of 10 m wind over complex terrain remain challenging, and the relative performance of nested regional and variable-resolution modelling strategies is still insufficiently documented. This study presents a controlled comparison of five deterministic forecast products over Türkiye: four nested WRF configurations at 12 km parent and 4 km child domain resolution with one-way and two-way nesting, and regional MPAS-Atmosphere with an approximately 4 km refined inner region. All experiments use identical 0.25° GFS forcing, are initialised daily at 00:00 UTC, and integrated for 48 h. After excluding the first 6 h as spin-up, verification is performed over lead times 6–47 h using 10 m wind speed observations from 509 TSMS stations during January and June 2023. Terrain effects are explicitly addressed through a Terrain Ruggedness Index computed from SRTM GL3 elevation data and a three-class terrain stratification. Model performance is assessed using mean error, mean absolute error, root mean square error, and Pearson correlation coefficient. MPAS achieves the lowest error across all aggregate metrics, with ME of 0.76 m s−1 and RMSE of 2.16 m s−1, compared to 0.99 m s−1 and 2.27 m s−1 for the best WRF configuration. All models exhibit systematic positive bias. The performance gap between MPAS and WRF is strongly terrain-dependent: negligible in low-complexity terrain but reaching 41 % ME reduction at high-complexity sites. Increasing WRF resolution from 12 km to 4 km does not consistently reduce bias, with the coarser 12 km two-way configuration outperforming the finer 4 km two-way configuration across both months and all terrain classes, pointing to grey-zone boundary layer limitations and lateral boundary constraints. Two-way nesting provides meaningful improvement at 12 km but offers diminishing returns at 4 km. MPAS’s aggregate ME advantage partly reflects a compensating bias structure: reduced overprediction at calm and moderate wind speeds is offset by stronger underprediction at high wind speeds, reaching ME of −3.28 m s−1 at high-complexity sites. This wind speed dependence also explains the spatial reversal along the Aegean coast, where WRF 4 km configuration outperforms MPAS at 70 % of stations. These results indicate that neither modelling approach offers a universal advantage; relative performance depends on terrain complexity, local wind climate, and the verification metric of interest.

Accurate deterministic forecasts of 10 m wind over complex terrain remain challenging, and the relative performance of nested regional and variable-resolution modelling strategies is still insufficiently documented. This study presents a controlled comparison of five deterministic forecast products over Türkiye: four nested WRF configurations at 12 km parent and 4 km child domain resolution with one-way and two-way nesting, and regional MPAS-Atmosphere with an approximately 4 km refined inner region. All experiments use identical 0.25° GFS forcing, are initialised daily at 00:00 UTC, and integrated for 48 h. After excluding the first 6 h as spin-up, verification is performed over lead times 6–47 h using 10 m wind speed observations from 509 TSMS stations during January and June 2023. Terrain effects are explicitly addressed through a Terrain Ruggedness Index computed from SRTM GL3 elevation data and a three-class terrain stratification. Model performance is assessed using mean error, mean absolute error, root mean square error, and Pearson correlation coefficient. MPAS achieves the lowest error across all aggregate metrics, with ME of 0.76 m s−1 and RMSE of 2.16 m s−1, compared to 0.99 m s−1 and 2.27 m s−1 for the best WRF configuration. All models exhibit systematic positive bias. The performance gap between MPAS and WRF is strongly terrain-dependent: negligible in low-complexity terrain but reaching 41 % ME reduction at high-complexity sites. Increasing WRF resolution from 12 km to 4 km does not consistently reduce bias, with the coarser 12 km two-way configuration outperforming the finer 4 km two-way configuration across both months and all terrain classes, pointing to grey-zone boundary layer limitations and lateral boundary constraints. Two-way nesting provides meaningful improvement at 12 km but offers diminishing returns at 4 km. MPAS’s aggregate ME advantage partly reflects a compensating bias structure: reduced overprediction at calm and moderate wind speeds is offset by stronger underprediction at high wind speeds, reaching ME of −3.28 m s−1 at high-complexity sites. This wind speed dependence also explains the spatial reversal along the Aegean coast, where WRF 4 km configuration outperforms MPAS at 70 % of stations. These results indicate that neither modelling approach offers a universal advantage; relative performance depends on terrain complexity, local wind climate, and the verification metric of interest.

Accurate deterministic forecasts of 10 m wind over complex terrain remain challenging, and the relative performance of nested regional and variable-resolution modelling strategies is still insufficiently documented. This study presents a controlled comparison of five deterministic forecast products over Türkiye: four nested WRF configurations at 12 km parent and 4 km child domain resolution with one-way and two-way nesting, and regional MPAS-Atmosphere with an approximately 4 km refined inner region. All experiments use identical 0.25° GFS forcing, are initialised daily at 00:00 UTC, and integrated for 48 h. After excluding the first 6 h as spin-up, verification is performed over lead times 6–47 h using 10 m wind speed observations from 509 TSMS stations during January and June 2023. Terrain effects are explicitly addressed through a Terrain Ruggedness Index computed from SRTM GL3 elevation data and a three-class terrain stratification. Model performance is assessed using mean error, mean absolute error, root mean square error, and Pearson correlation coefficient. MPAS achieves the lowest error across all aggregate metrics, with ME of 0.76 m s−1 and RMSE of 2.16 m s−1, compared to 0.99 m s−1 and 2.27 m s−1 for the best WRF configuration. All models exhibit systematic positive bias. The performance gap between MPAS and WRF is strongly terrain-dependent: negligible in low-complexity terrain but reaching 41 % ME reduction at high-complexity sites. Increasing WRF resolution from 12 km to 4 km does not consistently reduce bias, with the coarser 12 km two-way configuration outperforming the finer 4 km two-way configuration across both months and all terrain classes, pointing to grey-zone boundary layer limitations and lateral boundary constraints. Two-way nesting provides meaningful improvement at 12 km but offers diminishing returns at 4 km. MPAS’s aggregate ME advantage partly reflects a compensating bias structure: reduced overprediction at calm and moderate wind speeds is offset by stronger underprediction at high wind speeds, reaching ME of −3.28 m s−1 at high-complexity sites. This wind speed dependence also explains the spatial reversal along the Aegean coast, where WRF 4 km configuration outperforms MPAS at 70 % of stations. These results indicate that neither modelling approach offers a universal advantage; relative performance depends on terrain complexity, local wind climate, and the verification metric of interest.

Accurate deterministic forecasts of 10 m wind over complex terrain remain challenging, and the relative performance of nested regional and variable-resolution modelling strategies is still insufficiently documented. This study presents a controlled comparison of five deterministic forecast products over Türkiye: four nested WRF configurations at 12 km parent and 4 km child domain resolution with one-way and two-way nesting, and regional MPAS-Atmosphere with an approximately 4 km refined inner region. All experiments use identical 0.25° GFS forcing, are initialised daily at 00:00 UTC, and integrated for 48 h. After excluding the first 6 h as spin-up, verification is performed over lead times 6–47 h using 10 m wind speed observations from 509 TSMS stations during January and June 2023. Terrain effects are explicitly addressed through a Terrain Ruggedness Index computed from SRTM GL3 elevation data and a three-class terrain stratification. Model performance is assessed using mean error, mean absolute error, root mean square error, and Pearson correlation coefficient. MPAS achieves the lowest error across all aggregate metrics, with ME of 0.76 m s−1 and RMSE of 2.16 m s−1, compared to 0.99 m s−1 and 2.27 m s−1 for the best WRF configuration. All models exhibit systematic positive bias. The performance gap between MPAS and WRF is strongly terrain-dependent: negligible in low-complexity terrain but reaching 41 % ME reduction at high-complexity sites. Increasing WRF resolution from 12 km to 4 km does not consistently reduce bias, with the coarser 12 km two-way configuration outperforming the finer 4 km two-way configuration across both months and all terrain classes, pointing to grey-zone boundary layer limitations and lateral boundary constraints. Two-way nesting provides meaningful improvement at 12 km but offers diminishing returns at 4 km. MPAS’s aggregate ME advantage partly reflects a compensating bias structure: reduced overprediction at calm and moderate wind speeds is offset by stronger underprediction at high wind speeds, reaching ME of −3.28 m s−1 at high-complexity sites. This wind speed dependence also explains the spatial reversal along the Aegean coast, where WRF 4 km configuration outperforms MPAS at 70 % of stations. These results indicate that neither modelling approach offers a universal advantage; relative performance depends on terrain complexity, local wind climate, and the verification metric of interest.