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Regional precipitation patterns may change in a warmer climate, thereby increasing flood and drought risks. In this paper, annual, annual maximum, intense, heavy, moderate, light, and trace precipitation are employed as indicators to assess changes in precipitation patterns under two scenarios in which the global mean temperature increases by 1.5 °C and 2.0 °C relative to pre-industrial levels using the regional climate model COSMO-CLM (CCLM). The results show that annual precipitation in China will be approximately 2.5% higher under 1.5 °C warming relative to the present-day baseline (1980–2009), although it will decrease by approximately 4.0% under an additional 0.5 °C increase in global mean temperature. This trend is spatially consistent for regions with annual precipitation of 400–800 mm, which has experienced a drying trend during the past half century; thus, limiting global warming to 1.5 °C may mitigate these drying conditions. The annual maximum precipitation continues to increase from present day levels to the 2.0 °C warming scenario. Relative to the baseline period, the frequency of trace and light precipitation days exhibits a negative trend, while that of moderate, heavy, and intense precipitation days has a positive trend under the 1.5 °C warming scenario. For the 2.0 °C warming world, the frequency of days is projected to decrease for all precipitation categories, although the intensity of intense precipitation increases. Spatially, a decrease in the number of precipitation days is expected to continue in central and northern China, where a drying trend has persisted over the past half century. Southeastern China, which already suffers greatly from flooding, is expected to face more heavy and intense precipitation with an additional 0.5 °C increase in global mean temperature. Meanwhile, the intensity of intense precipitation is expected to increase in northern China, and the contribution of light and moderate precipitation to the annual precipitation is expected to decrease in southeastern China. Therefore, flood risk in northern China and drought risk in southern China should draw more attention for a global air temperature increase from 1.5 °C to 2.0 °C. [ABSTRACT FROM AUTHOR]

Climate change shifts the distributions of a set of climatic variables, including temperature, precipitation, humidity, wind speed, sunshine duration, and evaporation. This paper explores the importance of those additional climatic variables other than temperature and precipitation. Using the county-level agricultural data from 1980 to 2010 in China, we find that those additional climatic variables, especially humidity and wind speed, are critical for crop growth. Therefore, omitting those variables is likely to bias the predicted impacts of climate change on crop yields. In particular, omitting humidity tends to overpredict the cost of climate change on crop yields, while ignoring wind speed is likely to underpredict the effect. Our preferred specification indicates that climate change is likely to decrease the yields of rice, wheat, and corn in China by 36.25%, 18.26%, and 45.10%, respectively, by the end of this century. [ABSTRACT FROM AUTHOR]

This paper tries to answer the question that whether farmers can adjust better to climate change in the short-term than in long-term by using panel data models and long difference models respectively. We find that short term weather shocks are less detrimental to maize yield than the long-term climate changes, which can be seen as the evidences of adaptations. For adaptation options, we find farmers choose to decrease maize planting area or enlarge the irrigation inputs to cope with the increase of extreme heat days; when there are more precipitations, farmers will increase the input of fertilizer or labor. [ABSTRACT FROM AUTHOR]