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Monthly weather forecasts (MOFCs) were shown to have skill in extratropical continental regions for lead times up to 3 weeks, in particular for temperature and if weekly averaged. This skill could be exploited in practical applications for implementations exhibiting some degree of memory or inertia toward meteorological drivers, potentially even for longer lead times. Many agricultural applications fall into these categories because of the temperature-dependent development of biological organisms, allowing simulations that are based on temperature sums. Most such agricultural models require local weather information at daily or even hourly temporal resolution, however, preventing direct use of the spatially and temporally aggregated information of MOFCs, which may furthermore be subject to significant biases. By the example of forecasting the timing of life-phase occurrences of the codling moth (Cydia pomonella), which is a major insect pest in apple orchards worldwide, the authors investigate the application of downscaled weekly temperature anomalies of MOFCs for use in an impact model requiring hourly input. The downscaling and postprocessing included the use of a daily weather generator and a resampling procedure for creating hourly weather series and the application of a recalibration technique to correct for the original underconfidence of the forecast occurrences of codling moth life phases. Results show a clear skill improvement of up to 3 days in root-mean-square error over the full forecast range when incorporating MOFCs as compared with deterministic benchmark forecasts using climatological information for predicting the timing of codling moth life phases. [ABSTRACT FROM AUTHOR]

Global warming will lead to earlier beginnings and prolongation of growing seasons in temperate regions and will have pronounced effects on phenology and life-history adaptation in many species. These changes were not easy to simulate for actual phenologies because of the rudimentary temporal (season) and spatial (regional) resolution of climate model projections. We investigate the effect of climate change on the regional incidence of a pest insect with nearly worldwide distribution and very high potential for adaptation to season length and temperature - the Codling Moth, Cydia pomonella. Seasonal and regional climate change signals were downscaled to the hourly temporal scale of a pest phenology model and the spatial scale of pest habitats using a stochastic weather generator operating at daily scale in combination with a resampling approach for simulation of hourly weather data. Under future conditions of increased temperatures (2045-2074), the present risk of below 20% for a pronounced second generation (peak larval emergence) in Switzerland will increase to 70-100%. The risk of an additional third generation will increase from presently 0-2% to 100%. We identified a significant two-week shift to earlier dates in phenological stages, such as overwintering adult flight. The relative extent (magnitude) of first generation pupae and all later stages will significantly increase. The presence of first generation pupae and later stages will be prolonged. A significant decrease in the length of overlap of first and second generation larval emergence was identified. Such shifts in phenology may induce changes in life-history traits regulating the life cycle. An accordingly lifehistory adaptation in photoperiodic diapause induction to shorter day-length is expected and would thereby even more increase the risk of an additional generation. With respect to Codling Moth management, the shifts in phenology and voltinism projected here will require adaptations of plant protection strategies to maintain their sustainability. [ABSTRACT FROM AUTHOR]