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Quantifying and separating different sources of uncertainty helps to improve the understanding of the projected effects of climate change and can inform decision‐making in adaptation planning. This paper (1) evaluated four process‐based crop models; (2) assessed the effects of climate change on maize yield using climate change outputs from seven global climate models (GCMs) under three representative concentration pathways (RCPs); and (3) disaggregated the contributions of multiple crop models, GCMs and RCPs to overall uncertainty. All four models captured more than 80% of the variation in days to silking, maturity and yield, indicating reasonably reproduced observations. Similarly, the root mean square errors were moderate for days to silking and maturity (fewer than 4 days) and yield (0.5–0.7 t/ha). Overall, the results indicate that the models could assess grain yield at the study sites reasonably well. The results of the multiple models ensemble indicate that the maize yield will decrease by 9–11% with a probability of 72–80% on average during the period 2010–2039 relative to the baseline (1976–2005). The uncertainty in the maize‐yield simulations might arise mostly from the GCM models, followed by the crop models and RCPs, the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. The uncertainty in maize‐yield simulations might arise mostly from global climate models (GCMs), followed by crop models and representative concentration pathways (RCPs), the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. [ABSTRACT FROM AUTHOR]

The active phase of the East Asian summer monsoon (EASM) in Taiwan during May and June, known as Meiyu, produces substantial precipitation for water uses in all sectors of society. Following a companion study that analysed the historical increase in the Meiyu precipitation, the present study conducted model evaluation and diagnosis based on the EASM lifecycle over Taiwan. Higher and lower skill groups were identified from 17 Couple Model Intercomparison Project Phase 5 (CMIP5) models, with five models in each group. Despite the difference in model performance, both groups projected a substantial increase in the Meiyu precipitation over Taiwan. In the higher skill group, weak circulation changes and reduced low‐level convergence point to a synoptically unfavourable condition for precipitation. In the lower skill group, intensified low‐level southwesterly winds associated with a deepened upper level trough enhance moisture pooling. Thus, the projected increase in Meiyu precipitation will likely occur through the combined effects of (1) the extension of a strengthened North Pacific anticyclone enhancing southwesterlies; and (2) more systematically, the Clausius–Clapeyron relationship that increases precipitation intensity in a warmer climate. The overall increase in the Meiyu precipitation projected by climate models of variable performance supports the observed tendency toward more intense rainfall in Taiwan and puts its early June 2017 extreme precipitation events into perspective. [ABSTRACT FROM AUTHOR]

The past few decades have witnessed a change in precipitation patterns around the world. This change includes an increase in heavy precipitation and a decrease in light and moderate precipitation. Global/regional warming has been attributed as one of the major factors causing the observed change in precipitation patterns. Past studies over the Indian region have not focused much on quantification of the impact of warming on changes in precipitation patterns. The present research quantifies the impact of warming on precipitation patterns over the Indian region. For this purpose, rain gauge‐based high‐resolution gridded precipitation data from the India Meteorological Department (IMD) for 113 years between 1901 and 2013 are used to link the changes in precipitation patterns over India during the South West (SW) monsoon season with warming using a new method that focuses on inter‐annual differences rather than time series. It has been reported that during the SW monsoon period, there is an increase of about 51.85% ± 19% in the top 10% precipitation for each degree (K) increase in temperature. Decreases of about 15–30%/K in low and moderate precipitation have also been reported per 1 K increase in temperature. The impact of warming is more severe over Western and North Eastern India and during July and August. An increase in heavy rainy days of about 46.53% ± 14% per 1 K increase in temperature has been reported over the Indian region. Low and moderate rainy days show a decrease of about 24.67% ± 9.53% for a unit degree increase in temperature. Results reported in this research point out increasing trends in droughts and floods due to increased heavy precipitation and decreased low and moderate precipitation in a warming environment. Study focuses on examination of precipitation patterns in changing climate over India. Results report an increase in heavy precipitation and decrease in low and moderate precipitation. Impacts of ENSO and IOD have been examined separately. Seasonal variation of heavy precipitation anomaly over Indian region (I) along with global temperature anomaly. Smoothing has been done to eliminate the fluctuation. [ABSTRACT FROM AUTHOR]